This Wednesday, at the Google I/O Keynote, there wasn’t just one major revelation, but a series of incremental improvements across several Google’s product portfolios. And the best part of the story is all the improvements are driven by discoveries in artificial intelligence – the intelligence exhibited by machines.
Continue reading “More Powerful and Soon To Be Everywhere, Here’s All You Need to Know about AI”
Machine learning and big data are the new BIG things going around in the world. They are being used for myriad purposes – better AI, apt malware detection, smart messenger apps, and lot, lot more. Topping that, they are being utilized by the Pentagon to eradicate the foundations of Islamic State militants and make the world a safer (and better) place to live in.
This May, the Pentagon announced that it is undertaking its newly minted Algorithmic Warfare Cross Functional Team (AWCFT), codenamed Project Maven, with introducing Big Data and Machine Learning to boost the process of discovering actionable intelligence with the help of aerial imagery. “We’re not going to solve it by throwing more people at the problem…That’s the last thing that we actually want to do. We want to be smarter about what we’re doing,” Air Force Lt. Gen. John N.T. “Jack” Shanahan, director for defence intelligence for war fighter support told a leading defence news magazine.
Continue reading “Pentagon Fights Off ISIS with Machine Learning and Big Data”
Internet of Things (IoT) is the new buzz these days. The new tide of connectivity goes beyond smartphones and laptops. It includes smart homes, smart cities, smart cars, connected wearables and tries to provide a “Connected Life”. People are increasingly becoming aware of the applications of IoT in their daily lives.
However, little do they know about the application of IoT in Industries, commonly known as “Industrial IoT”. Through this blog, we would like to share our thoughts on how IoT can save time, energy and money in industries.
Notable research firm, Gartner in its research highlighted that a thirty-fold increase in Internet-connected physical devices by the year 2020 will significantly alter the mechanism in which supply chain operates. For quite some time, ERP and Supply Chain Management have been going hand-in-hand. However, IoT will revolutionize the entire supply chain management process by smartly connecting people, processes, data and things through sensors and devices.
Nowadays, every firm is trying to reduce its ecological footprint. IoT can be helpful in achieving this goal through smart energy. A bulb or tube light in the factory can switch on automatically as soon as a worker passes by and switch off once the worker has left. This will help in saving electricity costs.
IoT can be helpful in reducing the overall time taken in production of goods and services. For example- Setup time can be reduced by switching on the machines before the workers arrive at the factory, thanks to connected machines and smart phones. Inventory monitoring and tracking time can also be reduced through IoT. IoT can also be useful in managing the workflow in an event of accident at the factory. In case of an accident, an alarm can be rung in the factory, providing all the relevant details about the accident to the workers. The work can then by diverted through some other route, or some other worker can be employed as soon as possible in place of the injured worker. All this will save time.
Another use can be spending less time on searching for equipments at the workplace. Since equipments and devices are interconnected and geographically tagged, workers can find equipments more easily instead of searching them around. Also, if workers know a piece of equipment has location-tracking, it acts as a deterrent from potential theft (the National Retail Federation estimated that in 2011, employee theft cost companies a whopping $34.5 billion).
Thus, IoT offers great opportunities for the industries, which ensures better and faster production of goods and management of processes.
To learn more about IoT, take up courses on Machine Learning Using Python. Check out DexLab Analytics for further details on SAS training courses.
Interested in a career in Data Analyst?
To learn more about Data Analyst with Advanced excel course – Enrol Now.
To learn more about Data Analyst with R Course – Enrol Now.
To learn more about Big Data Course – Enrol Now.To learn more about Machine Learning Using Python and Spark – Enrol Now.
To learn more about Data Analyst with SAS Course – Enrol Now.
To learn more about Data Analyst with Apache Spark Course – Enrol Now.
To learn more about Data Analyst with Market Risk Analytics and Modelling Course – Enrol Now.
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.
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.
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!
Take machine Learning course from experts who possess incredible math skills! The Machine Learning course in India is offered by DexLab Analytics. For more details, go through our Machine Learning Certification course brochure uploaded on the website.
.
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!
Take our credit risk management courses in Delhi to know more about financial management with data driven insights.
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?
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.
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.
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.
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.
Interested in a career in Data Analyst?
To learn more about Data Analyst with Advanced excel course – Enrol Now.
To learn more about Data Analyst with R Course – Enrol Now.
To learn more about Big Data Course – Enrol Now.To learn more about Machine Learning Using Python and Spark – Enrol Now.
To learn more about Data Analyst with SAS Course – Enrol Now.
To learn more about Data Analyst with Apache Spark Course – Enrol Now.
To learn more about Data Analyst with Market Risk Analytics and Modelling Course – Enrol Now.
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.
Interested in a career in Data Analyst?
To learn more about Data Analyst with Advanced excel course – Enrol Now.
To learn more about Data Analyst with R Course – Enrol Now.
To learn more about Big Data Course – Enrol Now.To learn more about Machine Learning Using Python and Spark – Enrol Now.
To learn more about Data Analyst with SAS Course – Enrol Now.
To learn more about Data Analyst with Apache Spark Course – Enrol Now.
To learn more about Data Analyst with Market Risk Analytics and Modelling Course – Enrol Now.
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.
Enrol in the most comprehensive machine learning course in India with us.
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:
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.
This is the math skill to have for the 21st 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.
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.
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.
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.
.
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!
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.
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
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.
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.
Interested in a career in Data Analyst?
To learn more about Data Analyst with Advanced excel course – Enrol Now.
To learn more about Data Analyst with R Course – Enrol Now.
To learn more about Big Data Course – Enrol Now.To learn more about Machine Learning Using Python and Spark – Enrol Now.
To learn more about Data Analyst with SAS Course – Enrol Now.
To learn more about Data Analyst with Apache Spark Course – Enrol Now.
To learn more about Data Analyst with Market Risk Analytics and Modelling Course – Enrol Now.