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Artificial Intelligence: What the Future Holds for India, Next to US

Posted on May 2, 2017June 7, 2018 by Dexlab

Is artificial intelligence outgrowing human intelligence? Is AI becoming smarter than we are?

In the United States – the world’s unchallenged superpower, a new strange issue has popped up, which is being discussed on all the major interactive platforms, like books, talk-shows, YouTube, etc. but to no avail. The issue-in-question is addressed in the beginning of the blog.

Also read: Learn to Surf on the Three Waves of Artificial Intelligence

Will computer intelligence exceed human intelligence?

In India, this doesn’t seem to bother us much. For us, computers are electronic devices that we control, that we command. Our smartphones and tablets are treated as our servants and not our masters. But I wonder for how long will this persist? How long will we be able to refrain ourselves from being influenced by the West? Well, that’s another question to answer and let’s keep it for another day!

Also read: What Makes Artificial Intelligence So Incredibly Powerful?

In the US, some of the tech pundits are working tirelessly on the newer realms of AI each day and speculating what will happen when computer programmes finally overhauls human brain in thinking abilities. Intelligence is a set of information, and the potential to know how to use it.

Also read: How Machine Learning Training Course and AI Made Lives Easier

Since 1990, computer technology has evolved substantially and has become nifty in intelligence. Let’s take the example of self driving cars ploughing the American roads. A fully independent, self-driving car is no more a VFX-induced scene from a sci-fi fantasy movie; in two years or more, they will be found dominating the streets of the US and trust me they will be a reality! Sit in your car, read a book or sleep while your car reaches its destination on its own. This car will perform all those functions that you used to do, giving you a hiatus from driving!

Also read: DexLab Analytics’ Take on the scope of Artificial Intelligence: Its Humanity vs. Algorithms

The boons of AI don’t end here, it’s thriving and improving faster. Why? Because, humans need to address a whole lot of problems with the help of technology. From conducting complicated surgeries to developing hi-tech BI tools, the scope of computer intelligence is vast and still increasing.

Another crucial factor is that the human brain is limited and can only contain a fixed amount of cerebral cortex and related substances that helps us think and remember. Beyond that, there is no scope for expansion, but in computer technology, the sky is the limit. It is possible to create a computer as large as a 3-storeyed building and store humongous amount of data in it.

The research says within the next 25 years, computer intelligence will become so efficacious that it will leave behind man’s intelligence in every way. So, what are you waiting for you? Give your career a robust boost with R programming courses. Reach us at DexLab Analytics, a leading R language certification institute for more queries.

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Role of Self Service Analytics in Businesses

Posted on April 5, 2017May 23, 2020 by Dexlab

Self Service Analytics is proving useful for business users, who are working on business data without necessarily having a background in technology and statistics. It is essentially bridging the gap between trained data analysts and normal business users.

Following are the characteristics of Self Service Analytics:

Business Users Independence:

Self Service Analytics reduces dependency on IT and Data warehousing teams, thereby reducing the turnaround time for a request made by a business user.

It does so by continuously collating and loading real time data into a singular stream without disparity, which is easily accessible through browsers. Thus, it helps business users in taking decisions on Real-Time basis.

This feature benefits organizations because vital decisions made within time can be more profitable as compared to the traditional way of analysing data, which may not be a good idea in respect to the urgency constraint.

Easier and Reduced Cost of Operations:

Often, the company’s data are fragmented and widespread across various divisions. This increases the headache of channelling the data meaningfully and in a wholesome manner.

Further to this, preparing reports using this data becomes a cumbersome job for the IT department or the department, which is serving such request. Hence, it may lead to increased cost of time or decreased quality of efficiency at which the operations have to run. However, many a times, these reports fail to give an overview of the operations in an organisation.

Self-service BI integrates data from different systems and delivers a “Single Version of Truth”. Accessing this data and running computations on it requires only a browser for access and eliminates the need to install, maintain and administer large-footprint software clients on each user’s workstation.

If Self Service Analytics is hosted on SaaS, it will further reduce the cost of machinery and maintenance associated with it. The provision for usage can be increased or decreased in no time according to the usage pattern. This really means that Self Service Analytics helps you adapt with time and Pay-Per-Use model, which is a leading trend in most of the industries.

Resolving the conflict over accuracy:

Typically, a business user using Excel would have a local copy of data and run computations on it. He can merge and transform it by using various formulas and finally derive a conclusion.

This is dangerous because in live operations, data keeps changing and data integrity is at stake by working on local copies. Thus, accuracy in decision-making becomes a game of luck.

In Self Service BI, the data from the source is extracted, transformed and loaded into a unique data model, which goes with all operations. In this case, data integrity is assured. In addition, all business users have the same source of data, removing the risk that working with different local copies have.

Therefore, from the above stated facts, we can conclude that Self Service Analytics is a need for today’s businesses.

However, there are a few risks involved in Self Service Business Analytics:

Loose corporate governance and make data available to business users directly may be taken advantage of in an undue manner.
Business users may not be properly trained or skilled to make decisions.
Relying heavily on any tool without some real life experience and insight into the background of that data can result into an impaired decision-making.

If all the above-mentioned risks are mitigated and proper corporate governance structure is in place, Self Service Analytics can be very beneficial for the success of any organization.

To excel in Self-Service Analytics, why not take up Machine Learning courses in Delhi from DexLab Analytics! They are informative, interesting and elaborate.

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How Machine Learning Training Course and AI Made Lives Easier

Posted on April 3, 2017March 5, 2020 by Dexlab

Technological superiority, the rise of the machines and an eventual apocalypse are often highlighted in sci-fi Hollywood movies. The unfavorable impacts of machine learning and excessive dependence on artificial intelligence have always been the hot topic for several Hollywood blockbusters, since years. And people who watch such movies develop a perception that more the technical advancement, higher is the chances that it will ignite a war against humans.

However, in reality, away from the world of Hollywood and motion pictures, Machine Learning and Artificial Intelligence is creating a sensation! If we look past the hype of Hollywood movies, we will understand that the Rise of Machines is certainly not the end of the world or the harbinger of apocalypse but a window of opportunity to achieve technical convenience.

How Things Got Simpler Using Machine Learning Training Course

Though individual are reaping benefits from AI, but it is the business world that is deriving most of its benefits. You will find AI everywhere- from gaming parlors to the humongous amount of data piled in workstation computers. Extensive research is being carried out in this field and scientists and tech gurus are spending huge amount of time in making this improved technology reach the masses. Also, Google and Facebook have placed their high hopes on AI and have also started implementing it in their products and services. Soon, we will see how easily Machine Learning and AI will stream from one product to another.

Who Are The Best Users of Machine Learning?

Machine learning cannot be implemented by every SaaS. Then who can be the active users of machine learning? As stated by a spokesperson of a reputable AI company, the implementation of Machine Learning is suitable for companies that have massive amounts of historical data stored. To train a puppy, you need a handful of treats, similarly to tackle an algorithm you need a vast amount of human corrected error-free data.

Secondly, to get the taste of success the companies, who are thinking of implementing AI, need a proper business case. You need a proper plan before you start operating. Always question yourself, whether your machine learning algorithm will be able to reduce your costs, while offering better value. If yes, then it is a green signal for you!

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Can We Fight Discrimination With Better Machine Learning?

Posted on March 18, 2017November 17, 2018 by Dexlab

With the increase in use of machine learning, for taking important corporate as well as national operational decisions, it is important to set across some core social domains. They will work to make sure that these decisions are not biased with discrimination against certain categories whatever they may be applied into.

In this post, we will discuss the crucial matters of “threshold classifiers”, a part of some machine learning operations that is critical to the issues of discrimination. With a threshold classifier one can essentially make a yes/no decision, which in turn helps to put things in perspective with one category or the other. Here we will take a look at how these classifiers work, the ways in which they can potentially be biased and how one may be able to turn an unfair classifier into a much fairer one.

By opting for a course on Machine Learning Using Python, you will be able to grasp the subject matter of this topic better.

In order to provide an illustrative example, we will concentrate on loan granting scenarios where the bank may approve or deny a loan based on one single, number computed automatically like a Credit score.

In the above-mentioned diagram, the dark dots represent people who do pay off their loans and debts, while the lighter dots show those who would not. In an ideal scenario, we may get to work with statistics that cleanly distinguish the classes as in the left example. However, sadly this is far more common to see a situation wherein at the right where the group overlaps.

A standalone statistic can stand in for several different variables, and boiling them down to just one number. In case of the credit score, which is evaluated by looking at several numbers of factors, that include income, promptness in debt repayment and much more. The number might even correctly represent the likelihood that a person may pay off a debt or also default, or might not. This relationship is actually pretty blurred and it is rare to find a statistic that correlates perfectly with real-world outcomes.

And that is exactly where the idea of a “threshold classifier” comes in: the bank selects a particular cut-off or threshold, and the people who have their credit scores are mentioned below it, will be denied of loans and people above it are usually granted the lending. However, real banks have several more additional complexities, but this simple model is often useful for studying some of the fundamental issues. Also to be clear, Google does not use credit scores for their products!

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The above-mentioned diagram makes use of synthetic data to show how a threshold classifier works. For further simplification of the explanation, we will be staying away from realistic credit scores or the data what you see shows just the simulated data with a score based on the range of 0 to 100.

As can be well understood, selecting a threshold needs some tradeoffs. Too low and the bank wil l end up giving loans to many people who default; if too high many people who actually do deserve a loan will not get them.

So, how to determine the right threshold? That is subjective. One important goal may be to maximize the number of appropriate decisions. (Can you tell us what threshold will do that in this example scenario?)

Another financial situational goal may be to, maximize profit. At the bottom of the above mentioned diagram, is a readout hypothetical “profit” which is based on the model wherein a successful loan will make USD 300, but a default will cost a bank USD 700. So what will be the most profitable threshold? And does it match the threshold with the maximum correct decisions?

Discrimination and categorization:

The aspect of how to make a correct decision is defined, and with sensitivities to which factors will become particularly thorny, when a statistic like a credit score ends up distributed separately in between the two teams.

Let us imagine that we have two teams of people ‘orange’ and ‘blue’. We are keen on making small loans, subject to the following rules:

A successful loan will make USD 300
But an unsuccessful loan will make USD 700
Everyone will have a credit score of range 0 to 100

DexLab Analytics offers credit risk analysis course online for the ease of promoting financial credit risk knowledge and data analytics know-how to the right personnel conveniently.

How to simulate loan decisions for different groups:

Drag the black threshold bars either left or right to alter the cut-offs for loans. Click on the varying preset loan strategies:

In the above mentioned case, the distributions of the two groups are slightly varying. While the blue and the orange people are equivalently likely to pay off a debt. But if you take look for a pair of thresholds that maximize total profit (or click on max profit button), then you will be able to see that the blue group is held in a slightly higher standard than the orange one.

How to improve machine-learning systems:

An important outcome of the paper by Hardt, Price, and Srebro depicted that – when mentioned essentially in any scoring system, it will be possible to efficiently to find the thresholds that meet any of the above mentioned criteria. Put in other words, even if you do not posses control over the underlying scoring system (which is quite a common case) it will still be possible to attack the issue of discrimination.

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Uber: Pioneering Machine Learning into Everything it Does

Posted on March 14, 2017March 14, 2017 by Dexlab

Uber is here as a mobile app, which allows you to request for a ride, but this company has never deemed itself to be a mere transportation service provider, rather it prefers to call itself a technology service provider, more like some logistics company.

More than a year ago, Danny Lange was appointed as the head of Machine Learning at Uber and he along with his team associates started operations from San Francisco. Being an ardent believer of the benefits that Machine learning can bring upon the society, Lange considers that AI and Machine Learning, if combined together can absolutely solve any business discrepancies, irrespective of the nature of the problem.

Continue reading “Uber: Pioneering Machine Learning into Everything it Does”

Facebook is planning to evaluate its quest for generalised AI

Posted on March 13, 2017November 16, 2018 by Dexlab

A major misconception about artificial intelligence is the fact that today’s robots possess a very generalized intelligence, however, we are fairly efficient in leveraging large datasets to accomplish otherwise complex tasks. Nevertheless we still fail and fall flat at the prospect of replicating the breadth of human intelligence.

Care to contribute to AI development in today’s world? Then take up a Machine Learning course online with us. But in order to move forward a generalized intelligence, Facebook is ensure that we know how to evaluate the process. In a recently released paper, Facebook’s AI research (FAIR) lab has outlined just that as a part of its CommAI framework.

We will need our systems to be able to communicate and will be able to learn through language effectively even when they lack in context and discussing thing in undefined terms.

Furthermore, such systems should be capable of learning up new skills, fairly simply. As per Facebook this skill set is called “learning to learn”. Present machine learning models may be trained on data and be used for classifying defined objects. We can also make use of transfer learning to quickly adapt a model to achieve the same task on the new data, however our machines cannot completely teach themselves without heavy to moderate intervention from the developers.

It is in general agreed upon, that in order to generalize across several tasks, a program should be capable of compositional training. And that is of storing and recombination solutions to sub-problems across the different tasks, as per the team from Facebook.

As per Facebook they consider these capabilities to be of more of a prerequisite to being a generalized AI than the true Turing test. Alan Turing created the original Turing test in the 1950s. It is usually understood to be a means of assessing machine learning intelligence with respect to human intelligence.

However, with the maturation of the field of Ai the Turing test has lost a lot of its relevance. Facebook hopes to offer a nice alternative way to think about the necessary requirements of a modern generalized AI which should be less of a research distraction than the more rigid Turing Test.

The team at FAIR which include – Marco Baroni, Armand Joulin, Allan Jabri, Germán Kruszewski, Angeliki Lazaridou, Klemen Simonic and Tomas Mikolov have also developed another open source platform for the testing and training of AI systems.

For more information on Machine Learning training in Gurgaon or in Delhi NCR, drop by our institute at DexLab Analytics.

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The Math Behind Machine Learning: How it Works

Posted on February 17, 2017March 5, 2020 by Dexlab

$The Math Behind Machine Learning: How it Works$

It is evident that in the last few months, we have had several people showcase their enthusiasm about venturing into the world of data science using Machine Learning techniques. They are keen on probing the statistical regularities and building impeccable data-driven products. but we have made an observation that some may actually lack the necessary mathematical knowledge and intuition to get the framework for achieving results with data. And this is why we have decided to discuss this lacking through our blog.

In the recent times, there has been a noticeable upsurge in the availability of several easy-to-use machine and deep learning packages such as Weka, Tensorflow, scikit learn etc. But you must understand that machine learning as a field is one that has both statistical concepts, probabilistic concepts, computer science and algorithmic concepts to arise from learning intuitively from available data and also is about determining the patterns and hidden insights, which can be used to build intelligent applications. While still having the immense possibilities of Machine Learning and Deep Learning which is a thorough mathematical understanding of many of these techniques which is necessary for a good grasp of the internal workings of algorithms to achieve a good result.

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Why we must think about the math?

To explain why it is necessary to behind the scenes into the mathematical details of Machine Learning, we have put own a few important points:

To choose the right algorithm which will include giving considerations, to accuracy, to the right training time, complexity of model, number of parameters and the number of features.
To choose parameter settings and to validate the strategies
To indentify the under-fitting and over-fitting by understanding the bias-variance trade off.
For acquiring ample confidence about the interval and uncertainty

The level of math one will need:

The primary question when one tries to understand an interdisciplinary field such as Machine Learning, is the amount of math needed and the level of math needed to understand these techniques.

The answer to this question is not as simple as it may seem and is multidimensional which, depends upon the level and interest of the individual. Research conducted in these mathematical formulations and theoretical advancements for Machine Learning is an ongoing process and a few researchers are already working on few more advanced techniques. However, we will state the least amount of math that is a must have skill for being a successful Machine learning Engineer/ Scientist is the importance of each and every mathematical concept.

Linear algebra:

This is the math skill to have for the 21^st century. One must be well-versed with the topics of Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Eigendecomposition of a matrix, LU Decomposition, QR Decomposition/Factorization, Symmetric Matrices, Orthogonalization & Orthonormalization, Matrix Operations, Projections, Eigenvalues & Eigenvectors, Vector Spaces as these norms are absolutely necessary for the understanding and the optimization methods for machine learning. The best thing about linear algebra is that there are a lot of online resources.

Probability theory and statistics:

Machine learning and statistics are not too different a field. And in reality some people have actually defined Machine Learning as “doing statistics on a Mac”. A few fundamentals that are a must have for machine learning are – Combinatorics, Probability Rules & Axioms, Bayes’ Theorem, Random Variables, Variance and Expectation, Conditional and Joint Distributions, Standard Distributions (Bernoulli, Binomial, Multinomial, Uniform and Gaussian), Moment Generating Functions, Maximum Likelihood Estimation (MLE), Prior and Posterior, Maximum a Posteriori Estimation (MAP) and Sampling Methods.

Multivariate calculus:

Differential and Integral Calculus, Partial Derivatives, Vector-Values Functions, Directional Gradient, Hessian, Jacobian, Laplacian and Lagragian Distribution are some of the necessary topics necessary for understanding ML.

Algorithms and Complex Optimizations:

In order to realize the computational efficiency and scalability of our Machine Learning Algorithm and for exploiting the sparsity in the dataset, this concept is necessary. One must have knowledge of data structures such as Binary Trees, Hashing, Heap, Stack etc, and Dynamic Programming, Randomized & Sublinear Algorithm, Graphs, Gradient/Stochastic Descents and Primal-Dual methods.

A few other mathematical skills that are often necessary for understanding ML are the following Real and Complex Analysis (Sets and Sequences, Topology, Metric Spaces, Single-Valued and Continuous Functions, Limits), Information Theory (Entropy, Information Gain), Function Spaces and Manifolds.

Machine learning training in Gurgaon from experts with in-depth instruction on math skills is offered at DexLab Analytics. Check out our Machine learning certification brochure for the same at the website.

A robot too close to humans! Story of BINA 48

Posted on February 8, 2017February 8, 2017 by Dexlab

BINA 48 is the world’s most renowned and highly sought after humanoid robot in America. You can visit her there, by driving down a long winding dirt road just west of the Lincoln Gap in Bristol, Vt. Where sits two large yellow houses on a sprawling property that features ten solar panels and a dock over-looking the sunlit pond filled with trout, a homely porch decorated with rocking chairs.

In the smaller of the two houses resides BINA 48, who is one of the most sought after humanoid who is based on a real personality – Bina Rothblatt.

Continue reading “A robot too close to humans! Story of BINA 48”

How to Assess Clustering Tendency: Unsupervised Machine Learning

Posted on January 24, 2017November 7, 2019 by Dexlab

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”

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