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.
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In the coming years, jobs and businesses are going to be impacted; reason AI. Today’s generation is very much concerned about how the bots will consume everything; from jobs to skills, the smart machines will spare nothing! It is true that machines are going to replace man-powered jobs – by using robots, mundane jobs can be performed in a flick of an eye freeing people working in bigger organisations to innovate and succeed.
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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.
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!
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.
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!
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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In this New Age, businesses pay maximum attention in collecting customer and transactional data. Businesses with draconian financial reporting and persistent audit requirements look up to ETL, as it offer an organized and integrated solution instead of relying on other apparent solutions like Hadoop.
Continue reading “ETL and ETL Testing: The Concepts Explained”
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.
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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.
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.
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.
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.
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.
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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!
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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?
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.
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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.
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