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5 Hottest Online Applications Inspired by Artificial Intelligence

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5 Hottest Online Applications Inspired by Artificial Intelligence

Artificial Intelligence projects, applications and platforms are being churned out from every corner of the world. A majority of them now possess the ability to break loose lab life and hit mainstream trends, making an appearance in myriad online tools, open source APIs and mass gadgets.

Though the machines are yet to take over our lives, they are filtrating their way into our lives, influencing day-to-day activities, be it work or entertainment. From personal assistants like Alexa and Siri, to self-driving vehicles powered by predictive modeling and more intense and fundamental machine learning technologies, a wide set of applications of AI are in use of late.

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We perused through a handful number of AI apps so that we can enlist the ones that are more practical and thus really deserving! Let’s leverage piles of data with these effective applications:

Siri

As per Creative Strategies report, 70% of iPhone users have used Siri at least for once or sometimes, but everyone has tried it at least. We are here to tell you don’t hire a personal assistant, instead implement Siri.

siri

This voice-powered virtual assistant makes business operations smoother and hassle-free, while making your workday more productive. The software is activated by voice, and it is at present available in 20 languages.

Alexa

Developed and powered by Amazon for Amazon Echo intelligent speaker, Alexa, a robust voice service was launched in 2014. It can help you in ordering supplies, translating and controlling office’s vacuum.

amazon-echo

However, connecting your Echo to IFTTT may allow you to coordinate with services that aren’t supported originally by the Echo, while allowing you to integrate multiple actions into a single command to the Echo.

Google Now

This is one of the most popular artificial intelligence applications. Google Now functions by keeping a tab on your calendar, mail, web searches and lot more, along with sending relevant alerts and news on your device as and when detected. It can also carry out tasks, and answer queries, based on voice commands.

google-now

The best part of this application is that you don’t have to log in to use it. Just set up alerts that will be sent to the device, and that’s all. At present, it is available in English and is considered a tailing rival of Siri.

Cortana

If you know the exact way to maneuver it, Cortana would be the most effective AI personal assistant. It can perform all sorts of things, right from dictating and sending emails, tracking flights to searching something on the internet or checking weather forecasts. The more time you spent on it, its functionality gets better and better.

cortana

Even, the company is so impressed by its services that it has integrated the service into Power BI, its most intuitive BI tool.

Braina

Brain Artificial, aka Braina is self-regulating software, which enables easy hands-free operation in your computer to perform basic tasks by listening to voice based commands in English language.

braina-1

Braina enjoys a certain edger over its run of the mill competitors as it can precisely work with a variety of accents, which is not so common. The pro version is equipped with a bonus of deep learning – it is programmable as well as observes user behavior over time.

Hope, AI applications serves the humanity well!

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Automation Doesn’t Necessarily Make Humans Obsolete, Here’s Why

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Machines are going to eat our jobs.

 

AI is handling insurance claims and basic bookkeeping, maintaining investment portfolios, doing preliminary HR tasks, and performing extensive legal research and lot more. So, do humans stand a chance against the automation apocalypse, where everything, almost everything will be controlled by robots?

 
Automation Doesn’t Necessarily Make Humans Obsolete, Here’s Why
 

What do you think? You might be worried about your future job opportunities and universal basic income, but I would ask you to draw a clearer picture about this competing theory – because, in the end, this question might not even be a plausible and completely valid question. Why, I will tell you now.

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Top 10 Nifty Tools to Manage Big Marketing Data for Companies

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Big Data is the latest buzz. It has to be effectively analyzed to formulate brilliant marketing and sales strategies. It’s of immense importance, as it includes humongous amount of information accumulated about customers from numerous sources like email marketing schemes and web analytics.

 
Top 10 Nifty Tools to Manage Big Marketing Data for Companies
 

However, due to the vast magnitude of information available, it may get quite difficult for marketers to analyze and evaluate all the data in an efficient way. Fortunately, plenty of tools are available in the market that can manage mammoth marketing data and here are few of them:

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Facebook Shut Down AI amid Fears of Losing Control

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Facebook Shut Down AI amid Fears of Losing Control
 

Analysts at Facebook promptly shut down the Artificial Intelligence system over concerns they might lose control over the system. Recently, Facebook had developed a new Artificial Intelligence program, which could create its own language with the help of code words to make communication easier and effective. The researchers took it offline, when they understood the language used is no longer English.

 

fb_ai-657x360-702x336

 

Though this isn’t the first time that AIs went a step ahead to take a different route instead of the oh-so-regular training in English language to develop their own more productive language, the recent Facebook incident made us wary about Elon Musk’s warnings about AI. “AI is the rare case where I think we need to be proactive in regulation instead of reactive,” Musk, co-founder, CEO and Product Architect at Tesla once stated at the meet of US National Governors Association. “Because I think by the time we are reactive in AI regulation, it’ll be too late,” he further added.

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INTCK and INTNX: All about SAS Dates and Computing Intervals between Dates

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INTCK-and-INTNX

The INTCK and INTNX functions in SAS helps you compute the time between events. This technical blog is based on the timeline of living US presidents, sourced from a Wikipedia table. The table data shows the number of years and days between events.

So, let’s start.

LivingPresidents2

Gaps between dates

To calculate the interval between two dates, you can use these two SAS functions:

The INTCK function returns the number of time units between dates. The time unit can be selected in years, months, weeks, days, or whatever you feel like.

The INTNX function helps you compute the date that is 308 days away in the future from a specific date. This was just an example to help you understand what it means. The INTNX function returns a SAS date that is particular number of time units away from a particular date.

These two functions share a complimentary bond: where one calculates the difference between two dates, the other entitles you to add time units to a specified date value. Also, the INT part in both the functions denotes INTervals, and the terms INTCK and INTNX means Interval Check and Interval Next, respectively.

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How to calculate anniversary dates

These two prime functions tend to be useful in counting the number of anniversaries between two dates along with calculating a future anniversary date. Use the ‘CONTINUOUS’ option for the INTCK function and the ‘SAME’ option for the INTNX function in the following manner:

The ‘CONTINUOUS’ option in the INTCK function helps you count the number of anniversaries of one date that occur before a second date. For example, the statement

Years = intck('year', '30APR1789'd, '04MAR1797'd, 'continuous');

returns the value 7 because there are 7 full years (anniversaries of 30APR) between those two dates. Without the ‘CONTINUOUS’ option, the function returns 8 as 01JAN occurs 8 times between those dates.

The statement

Anniv = intnx('year', '30APR1789'd, 7, 'same');

returns the 7th anniversary of the date 30APR1789. In some ways, it returns the date value for 30APR1796.

The most exciting part about these two functions is that they automatically handle leap years! Yes, you read that right. If you ask for the number of days within two dates, the INTCK function will show leap days in the result. If an event takes place on a leap day, and you ask the INTNX function to reveal the anniversary date, it will report 28FEB of the next year to the next anniversary date.

An algorithm calculating years and days between events

Go through the following algorithm to calculate the number of years and days between dates in SAS:

  • Use the INTCK function with the ‘CONTINUOUS’ option to calculate the number of completed years between two dates
  • Use the INTNX function to discover a third date, i.e. anniversary date, which is the same month and day like the start date, but takes place less than a year before the end date.
  • Use the INTCK function to ascertain the number of days occurring between the anniversary date and the end date.

Here are the data steps that enable you to compute the time interval in years and days between the first few US presidential inaugurations and deaths.

data YearDays;
format Date prevDate anniv Date9.;
input @1  Date anydtdte12.
      @13 Event $26.;
prevDate = lag(Date);
if _N_=1 then do;                               /* when _N_=1, lag(Date)=. */
   Years=.; Days=.; return;            /* set years & days, go to next obs */
end;
Years = intck('year', prevDate, Date, 'continuous'); /* num complete years */
Anniv = intnx('year', prevDate, Years, 'same');      /* most recent anniv  */
Days = intck('day', anniv, Date);                    /* days since anniv   */
datalines;
Apr 30, 1789 Washington Inaug
Mar 4, 1797  J Adams Inaug
Dec 14, 1799 Washington Death
Mar 4, 1801  Jefferson Inaug
Mar 4, 1809  Madison Inaug
Mar 4, 1817  Monroe Inaug
Mar 4, 1825  JQ Adams Inaug
Jul 4, 1826  Jefferson Death
Jul 4, 1826  J Adams Death
run;
 
proc print data=YearDays;
var Event prevDate Date Anniv Years Days;
run;

 

LivingPresidents3

 

In a nutshell, the INTCK and INTNX functions are consequential for calculating intervals between dates. In this blog, I discussed about two-less-popular options inn SAS, for more such SAS training related blogs, follow us at DexLab Analytics.

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This post originally appeared on – blogs.sas.com/content/iml/2017/05/15/intck-intnx-intervals-sas.html
 

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5 New-Age IT Skill Sets to Fetch Bigger Paychecks in 2017

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Technology is the king. It is slowly intensifying its presence over workplaces, and is one of the chief reasons why companies are laying off employees. Adoption of cutting-edge technologies is believed to be the main reason of job cuts and by now if professional techies are not properly equipped with newer technologies under their sleeves, the future of human workforce seems bleaker.

 
5 New-Age IT Skill Sets to Fetch Bigger Paychecks in 2017
 

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A recent report says – India would lose about 69,000 jobs until 2021 due to the adoption of IoT, so do you really think human intelligence is losing its intellect? Will AI finally surpass brain power?

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The Evolution of Neural Networks

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The Evolution of Neural Networks

Recently, Deep Learning has gone up from just being a niche field to mainstream. Over time, its popularity has skyrocketed; it has established its position in conquering Go, learning autonomous driving, diagnosing skin cancer, autism and becoming a master art forger.

Before delving into the nuances of neural networks, it is important to learn the story of its evolution, how it came into limelight and got re-branded as Deep Learning.

The Timeline:

Warren S. McCulloch and Walter Pitts (1943): “A Logical Calculus of the Ideas Immanent in Nervous Activity”

Here, in this paper, McCulloch (neuroscientist) and Pitts (logician) tried to infer the mechanisms of the brain, producing extremely complicated patterns using numerous interconnected basic brain cells (neurons).  Accordingly, they developed a computer-programmed neural model, known as McCulloch and Pitt’s model of a neuron (MCP), based on mathematics and algorithms called threshold logic.

2

Marvin Minsky (1952) in his technical report: “A Neural-Analogue Calculator Based upon a Probability Model of Reinforcement”

Being a graduate student at Harvard University Psychological Laboratories, Minsky executed the SNARC (Stochastic Neural Analog Reinforcement Calculator). It is possibly the first artificial self-learning machine (artificial neural network), and probably the first in the field of Artificial Intelligence.

Marvin Minsky & Seymour Papert (1969): “Perceptron’s – An Introduction to Computational Geometry” (seminal book):  

In this research paper, the highlight has been the elucidation of the boundaries of a Perceptron. It is believed to have helped usher into the AI Winters – a time period of hype for AI, in which funds and publications got frozen.

Kunihiko Fukushima (1980) – “Neocognitron: A Self-organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position” (this concept is an important component for Convolutional Neural Network – LeNet)

Fukushima conceptualized a whole new, much improved neural network model, known as ‘Neocognitron’. This name is derived from ‘Cognitron’, which is a self-organizing multi layered neural network model proposed by [Fukushima 1975].

David B. Parker (April 1985 & October 1985) in his technical report and invention report – “Learning – Logic”

David B. Parker reinvented Backpropagation, by giving it a new name ‘Learning Logic’. He even reported it in his technical report as well as filed an invention report.

Yann Le Cun (1988) – “A Theoretical Framework for Back-Propagation”

You can derive back-propagation through numerous ways; the simplest way is explained in Rumelhart et al. 1986. On the other hand, in Yann Le Cun 1986, you will find an alternative deviation, which mainly uses local criteria to be minimized locally.

 

J.S. Denker, W.R. Garner, H.P. Graf, D. Henderson, R.E. Howard, W. Hubbard, L.D. Jackel, H.S. Baird, and I. Guyon at AT&T Bell Laboratories (1989): “Neural Network Recognizer for Hand-Written ZIP Code Digits”

In this paper, you will find how a system ascertains hand-printed digits, through a combination of neural-net methods and traditional techniques. The recognition of handwritten digits is of crucial notability and of immense theoretical interest. Though the job was comparatively complicated, the results obtained are on the positive side.

Yann Le Cun, B. Boser, J.S. Denker, D. Henderson, R.E. Howard, W. Hubbard, L.D. Jackel at AT&T Bell Laboratories (1989): “Backpropagation Applied to Handwritten ZIP Code Recognition”

A very important real-world application of backpropagation (handwritten digit recognition) has been addressed in this report. Significantly, it took into account the practical need for a chief modification of neural nets to enhance modern deep learning.

Besides Deep Learning, there are other kinds of architectures, like Deep Belief Networks, Recurrent Neural Networks and Generative Adversarial Networks etc., which can be discussed later.

For comprehensive Machine Learning training Gurgaon, reach us at DexLab Analytics. We are a pioneering data science online training platform in India, bringing advanced machine learning courses to the masses.

 

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The Timeline of Artificial Intelligence and Robotics

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The Timeline of Artificial Intelligence and Robotics

Cities have been constructed sprawling over the miles, heaven-piercing skyscrapers have been built, mountains have been cut across to make way for tunnels, and rivers have been redirected to erect massive dams – in less than 250 years, we propelled from primitive horse-drawn carts to autonomous cars run on highly integrated GPS systems, all because of state-of-the-art technological innovation. The internet has transformed all our lives, forever. Be it artificial intelligence or Internet of Things, they have shaped our society and amplified the pace of high-tech breakthroughs.

One of the most significant and influential developments in the field of technology is the notion of artificial intelligence. Dating back to the 5th century BC, when Greek myths of Hephaestus incorporate the idea of robots, though it couldn’t be executed till the Second World War II, artificial intelligence has indeed come a long way.

 

Come and take a look at this infographic blog to view the timeline of Artificial Intelligence:

 

Evolution of Artificial Intelligence Over the Ages from Infographics

 

In the near future, AI will become a massive sector brimming with promising financial opportunities and unabashed technological superiority. To find out more about AI and how it is going to impact our lives, read our blogs published at DexLab Analytics. We offer excellent Machine Learning training in Gurgaon for aspiring candidates, who want to know more about Machine Learning using Python.

 

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The ABC of Summary Statistics and T Tests in SAS

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The ABC of Summary Statistics and T Tests in SAS

Getting introduced to statistics for SAS training? Then, you must know how to create summary statistics (such as sample size, mean, and standard deviation) to test hypotheses and to figure confidence intervals. In this blog, we will show you how to furnish summary statistics (instead of raw data) to PROC TTEST in SAS, how to develop a data set that includes summary statistics and how to run PROC TTEST to calculate a two-sample or one-sample t test for the mean.

So, let’s start!

2

Running a two-sample t test for difference of means from summarized statistics

Instead of going the clichéd way, we will start with establishing a comparison between the mean heights of 19 students, based on gender – the data is held in the Sashelp class data set.

Observe the below SAS statements that sorts the data by the grouping variable, calling PROC MEANS and printing a subset of the statistics:

proc sort data=sashelp.class out=class; 
   by sex;                                /* sort by group variable */
run;
proc means data=class noprint;           /* compute summary statistics by group */
   by sex;                               /* group variable */
   var height;                           /* analysis variable */
   output out=SummaryStats;              /* write statistics to data set */
run;
proc print data=SummaryStats label noobs; 
   where _STAT_ in ("N", "MEAN", "STD");
   var Sex _STAT_ Height;
run;

summarystats1

The table reflects the structure of the Summary Stats set for two sample tests. The two samples used here are differentiated on the levels of the Sex Variable (‘F’ for females and ‘M’ for males). The _STAT_ column shows the name of the statistic implemented here. The Height column depicts the value of the statistics for individual group.

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The problem: The heights of sixth-grade students are normally distributed. Random samples of n1=9 females and n2=10 males are selected. The mean height of the female sample is m1=60.5889 with a standard deviation of s1=5.0183. The mean height of the male sample is m2=63.9100 with a standard deviation of s2=4.9379. Is there evidence that the mean height of sixth-grade students depends on gender?

Here, you have to do nothing special to get the PROC TTEST – whenever the procedure gets the sight of the respective variable _STAT_ and any unique values, the procedure understands that the data set comprises summarized statistics. The following representation compares the mean heights of males and females:

proc ttest data=SummaryStats order=data
           alpha=0.05 test=diff sides=2; /* two-sided test of diff between group means */
   class sex;
   var height;
run;

summarystats1

Check the confidence intervals for the standard deviations and also that the output includes 95% confidence intervals for group means.

In the second table, the ‘Pooled’ row radiates out the impression that both the variances of two groups are more or less equal, which is somewhat true even. The value of the t statistic is t = -1.45 with a two-sided p-value of 0.1645.

The syntax for the PROC TTEST statement allows you to change the type of hypothesis test and the significance level. To support this, you can now run a one-sided test for the alternative hypothesis μ1 < μ2 at the 0.10 significance level just by using:

proc ttest ... alpha=0.10 test=diff sides=L;  /* Left-tailed test */

Running a one-sample t test of the mean from summarized statistics

In the above section, you have learnt to create the summary statistics from PROC MEANS. Nevertheless, you can also generate the summary statistic manually, if you lack original data.

The problem: A research study measured the pulse rates of 57 college men and found a mean pulse rate of 70.4211 beats per minute with a standard deviation of 9.9480 beats per minute. Researchers want to know if the mean pulse rate for all college men is different from the current standard of 72 beats per minute.

The following statements jots down the summary statistics for a data set, asks PROC TTEST to perform a one-sample test of the null hypothesis μ = 72 against a two-sided alternative hypothesis:

data SummaryStats;
  infile datalines dsd truncover;
  input _STAT_:$8. X;
datalines;
N, 57
MEAN, 70.4211
STD, 9.9480
;
 
proc ttest data=SummaryStats alpha=0.05 H0=72 sides=2; /* H0: mu=72 vs two-sided alternative */
   var X;
run;

summarystats3 (2)

The outcome is a 95% confidence interval for the mean containing a value 72. The value of the t statistic is t = -1.20, which corresponds to a p-value of 0.2359. Therefore, the data fails in rejecting the null hypothesis at the 0.05 significance level.

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This post originally appeared on – blogs.sas.com/content/iml/2017/07/03/summary-statistics-t-tests-sas.html
 

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