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Google’s DeepMind: Roll the Wheels of Imagination with Advanced AI

Posted on July 28, 2017May 23, 2020 by Dexlab

Use intelligence to make the world a better place to live in – Google’s London-based AI coterie, DeepMind is a pioneer in artificial intelligence research programs and has churned out two distinct types of AI that uses the ‘power of imagination’ to plan ahead and fulfil tasks with a higher success rate than the previous ones that lacked imagination.

In a recent interview, DeepMind researchers shared a crisp review of “a new family of approaches for imagination-based planning.” I2As, the so-called Imagination-Augmented Agents make use of an internal ‘imagination encoder’, which helps the AI determine what are and what aren’t productive predictions about its atmosphere.

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Data Journalism: What is it and how it works

Posted on July 27, 2017July 27, 2017 by Dexlab

The internet has killed some newspapers’ lunch, but it also presented them something truly remarkable – Data Journalism.

Introducing Data Journalism

Data journalism is an amalgamation of a nosy reporter’s news sniffing capabilities and a statistician’s fondness for data analysis. By scrounging through vast amounts of data sets that are available through extensive connectivity, data journalists are using this data to etch out interesting stories.

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Business Intelligence: Now Every Person Can Use Data to Make Better Decisions

Posted on July 26, 2017May 23, 2020 by Dexlab

The fascinating world of Business Intelligence is expanding. The role of data scientists is evolving. The mysticism associated with data analytics is breaking off, making a way for non-technical background people to understand and dig deeper into the nuances and metrics of data science.

“Data democratization is about creating an environment where every person who can use data to make better decisions, has access to the data they need when they need it,” says Amir Orad, CEO of BI software company Sisense. Data is not to be limited only in the hands of data scientists, employees throughout the organization should have easy access to data, as and when required.

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The Evolution of Neural Networks

Posted on July 25, 2017November 24, 2018 by Dexlab

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.

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.

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The Timeline of Artificial Intelligence and Robotics

Posted on July 21, 2017September 13, 2018 by Dexlab

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 5^th 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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Indian Startups Relying on Artificial Intelligence to Know Their Customer’s Better

Posted on July 19, 2017October 1, 2018 by Dexlab

Artificial Intelligence was there decades ago, but everyone is talking about AI and Big Data in India’s startup ecosystem of late.

Budding startups are looking for new talent with AI expertise to inspect and evaluate consumer data and provide customized services to the users. At the same time, tech honchos such as Apple have discovered the huge potentials hidden within Indian companies that help their clients with data processing, image and voice recognition, and no wonders, investors are too hopeful for Indian AI startups.

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Here are a slew of Indian unicorns – companies valued at $1 billion or more that are putting in use the exploding technology of AI in the best way possible:

Paytm

An eye-piercing transformation from being an e-wallet to selling flight or movie tickets, Paytm is now implementing machine learning to bring order into chaos. The company’s chief technology offer, Charumitra Pujari, said, “You could Google and try to look for something. But a better world would be when Google could on its own figure out Charu is looking for ‘x’ at this time. That’s exactly what we’re doing at Paytm,” he further added, “If you’ve come to buy a flight ticket, because I understand your purchase cycle, I show that instead of a movie ticket or transactions.”

In order to identify and prevent fraudulent activities, machines are constantly assessing illicit accounts that purposefully sign up to derive advantage of promo codes, or for money laundering intention. The fraud-detection engine is extremely efficient, leaving no room for human error, Pujari stated.

The team at Paytm is versatile – machine learning engineers, software engineers, and data scientists are in action in Toronto, Canada, as well as in Paytm’s headquarters in Noida, India. Currently, they have 60 people working for them in each location – “We know the future is AI and we will need a lot more people,” said Pujari.

Ola cabs

One of the most successful ride-hailing apps in India, Ola uses machine learning tech to track traffic, crack through driver habits, improve customer experience and enhance the life of each vehicle they acquired. AI plays a consequential role in interpreting day-in-day-out variations in demand and to decipher how much supply is required to cater to its increased demand, how variable are traffic predictions and how rainfall affects the productiveness of vehicles.

“AI is understanding what is the behavioral profile of a driver partner and, hence, in which way can we train him to be a better driver partner on (the) platform,” co-founder and chief technology officer Ankit Bhati said, the algorithms put into the car-pooling service works great in pulling down travel times by coordinating with various pick-up points and destinations, while sharing one single vehicle, he further added.

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Flipkart

According to a report in Forbes, Flipkart – India’s largest domestic e-commerce player has already re-designed its app’s home screen to give a more personalized version of services to its mushrooming 120 million patrons. Machine learning models crack each customer’s gender, brand preference, store affinity, price range, volume of purchases and more. In fact, in future, the company is going forward to figure out the reasons about when and why the returns are made, and as a result will try to reduce their happenings.

A squad of 25 data scientists at Flipkart have started using AI to observe the past buyer behavior to predict their future purchases. “If a customer keys in a query for running shoes, we show only the category landing pages of the particular brand the customer wants to see, in the price point and styles that (are) preferred, as gauged by previous buying behaviour, therefore ensuring a faster, smoother checkout process,” Ram Papatla, the vice president of product management at Flipkart, said recently at an interview with a leading daily.

ShopClues, InMobi, SigTuple and EdGE Network are myriad other Indian startup players who are making it really big by utilizing the powerful tentacles of AI and machine learning.

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Data Says This Game of Throne Character Carries the Maximum Weight

Posted on July 18, 2017May 23, 2020 by Dexlab

HBO’s fiery fantasy saga Game of Thrones Season 7 premiered last Sunday – by now the ardent stalwarts of this epic series have figured out the characters that matter the most. Just recently, a reputable data analytics firm Looker revealed some interesting facts, based on the data accumulated. So, want to know who secured the topmost rank?

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The ABC of Summary Statistics and T Tests in SAS

Posted on July 17, 2017November 8, 2019 by Dexlab

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!

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;

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;

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;

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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Google Is All Set to Wipe Off Artificial Stupidity

Posted on July 15, 2017September 17, 2018 by Dexlab

Well, human-AI relation needs to improve. Amazon’s Alexa personal assistant is operating in one of the world’s largest online stores and deserves accolade as it pulls out information from Wikipedia. But what if it can’t play that rad pop banger you just heard and responds saying “I’m sorry, I don’t understand the question,”!! Disappointing, right?

All revered digital helpmates including Google’s Google Assistant and Apple’s Siri are capable of producing frustrating coups that can feel like artificial stupidity. Against this, Google has decided to start a new research push to realize and improve the existing relations between humans and AI. PAIR, for People + AI Research initiative was announced this Monday, and it would be shepherded by two data viz crackerjacks, Fernanda Viégas and Martin Wattenberg.

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Virtual assistants don’t like to be defeated – they get infuriated when they fail to perform a given task. In this context, Viégas says she is keen to study how people outline expectations regarding what systems can and cannot outperform a command – which is to say how virtual assistants should be designed to prick us toward only asking things that it can perform, leaving no room for disappointment.

Making Artificial Intelligence more transparent among people and not just professionals is going to be a major initiative of PAIR. It also released two open source tools to help data scientists grasp the data they are feeding into the Machine Learning systems. Interesting, isn’t it?

The deep learning programs that have recently gained a lot of appreciation in analyzing our personal data or diagnosing life-threatening diseases is of late said to be dubbed as ‘black boxes’ by polemicist researchers, meaning it can be trickier to observe why a system churn out a specific decision, like a diagnosis. So, here lies the problem. In life and death situations inside clinics, or on-road, while driving autonomous vehicles, these faulty algorithms may pose potent risks. Viégas says “The doctor needs to have some sense of what’s happening and why they got a recommendation or prediction.”

Google’s project comes at a time when the human consequences of AI are being questioned the most. Recently, the Ethics and Governance of Artificial Intelligence Fund in association with the Knight Foundation and LinkedIn cofounder Reid Hoffman declared $7.6 million in grants to civil society organizations to review the changes AI is going to cause in labor markets and criminal justice structures. Similarly, Google announces most of PAIR’s work will take place in the open. MIT and Harvard professors Hal Abelson and Brendan Meade are going to join forces with PAIR to study how AI can improve education and science.

Closing Thoughts – If PAIR can integrate AI seamlessly into prime industries, like healthcare, it would definitely shape roads for new customers to reach Google’s AI-centric cloud business destination. Viégas reveals she will also like to work closely with Google’s product teams, like the ones responsible for developing Google Assistant. According to her, such collaborations are great and comes with an added advantage, as it keeps people hooked to the product, resulting in broader company services. PAIR is a necessary shot to not only help push the society to understand what’s going on between humans and AI but also to boost Google’s bottom line.

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