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Category: Artificial Intelligence

Keep Pace with Automation: Emerging Data Science Jobs in India

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Indian IT market is not yet doomed. In fact, if you look at the larger picture, you will find India is expected to face a shortage of 200000 data scientists by 2020. Where traditional IT jobs are going through a rough patch, new age jobs are surfacing up, according to market reports. Big Data, Artificial Intelligence, the Internet of Things, Cloud Computing, and Cybersecurity are new digital domains that are replacing the old school jobs, like data entry and server maintenance, which are expected to reduce more over the next five years.
The next decade is going to witness most vacancies in these job posts:

However, just because there is a wide array of openings for a web services consultant doesn’t make it the most lucrative job position. Big Data architect job openings are much less in number, but offer handsome pays, according to reports.

A median salary of a web services consultant is Rs 9.27 lakh ($14,461) annually

A median salary of a big data architect is Rs 20.67 lakh ($32,234) annually

Now, tell me, which is better?

As technologies evolve so drastically, it becomes an absolute imperative for the techies to update their skills through short learning programs and crash courses. Data analyst courses will help them to sync in with the latest technological developments, which happens every day, something or the other. Moreover, it’s like a constant process, where they have to learn something every year to succeed in this rat race of technological superiority. Every employee needs to make some time, as well as the companies. The companies also need to facilitate these newer technologies in their systems to keep moving ahead of their tailing rivals.

Re-skill or perish – is the new slogan going around. The urgency to re-skill is creating a spur among employees with mid-level experience. If you check the surveys, you will find around 57% of the 7000 IT professionals looking forward to enroll for a short time learning course have at least 4 to 10 years of work experience. Meanwhile, a mere 11% of those who are under 4 years of experience are looking out for such online courses. It happens because, primary-stage employees are mostly fresh graduates, who receives in-house training from their respective companies, hence they don’t feel the urge to scrounge through myriad learning resources, unlike their experienced counterparts.

 

 

Today, all big companies across sectors are focusing their attention on data science and analytics, triggering major reinventions in the job profile of a data analyst. Owing to technology updates, “The role of a data analyst is itself undergoing a sea change, primarily because better technology is available now to aid in decision-making,” said Sumit Mitra, head of group human resources and corporate services at GILAC. To draw a closure, data science is the new kid in the block, and IT professionals are imbibing related skills to shine bright in this domain. Contact DexLab Analytics for data analyst course in Delhi. They offer high-in demand data analyst certification courses at the most affordable prices.

 

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.

 

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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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Artificial Intelligence: Let’s Crack the Myths and Unfold the Future to You

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Artificial Intelligence: Let’s Crack the Myths and Unfold the Future to You

A lot of myths are going around about Artificial Intelligence.

In a recent interview, Alibaba founder Jack Ma said AI can pose a massive threat to jobs around the world, along with triggering World War III. The logic of shared by him explained that in 30 years, humans will be working for only 4 hours a day, and 4 days a week.

Fuelling this, Recode founder Kara Swisher vouched for Ma’s prediction. She supported him by saying Ma is “a hundred percent right,” adding that “any job that’s repetitive, that doesn’t include creativity, is finished because it can be digitized” and “it’s not crazy to imagine a society where there’s very little job availability.” 

Besides, I find all these stuffs quite baffling. I think that if AI is going to be the driving force towards innovation and bringing in a new technological revolution, it’s upon US to curate the opportunities that will require new jobs. Apocalyptic predictions just don’t help.

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Let’s highlight the myths and the logical equations:

Myth 1: AI is going to kill our jobs – it can never happen

Remember, it’s humans who have created robots. We excel at mechanizing, systematizing and automating. We spurred the automation drive, while infusing intelligence to the machines.

The present objective is to create AIs that can work together with human intelligence to develop new narratives for problems we are yet to solve. To solve these new problems, we need new kinds of jobs – there’s a great scope of opportunity, let’s not believe that AI will kill our jobs.

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Myth 2: Robots are AI. Not at all.

From drones to self-organizing shelves in warehouses to machines sent to Mars, all are just machines programmed to function.

Myth 3: Big Data and Analytics are AI. Who said that?

Data mining, Data Science, Pattern Recognition – they are just human-created models. They might be intricate or complicated in nature, but not AI. Data and AI are two entirely different and divergent concepts.

Myth 4: Machine Learning and Deep Learning are AI. Again a big NO.

Though Machine Learning and Deep Learning are a part of the enormous AI tool kit, they are not AI. They are just mere tools to program computers to tackle complex patterns- like the way your email filters out spam by “understanding” what hundreds and thousands of users have identified as spam. They look uber smart, undeniably, in fact scary at times, when a computer wins against a renowned expert at the game GO, but they are definitely not AI.

Myth 5: AI includes Search Engines. Definitely NO.

Search Engines have made our lives easier, undoubtedly. The way you can search information now was impossible few years back, but being the searcher, you too contribute the intelligence. All the computer does is identify patterns from what you search and suggest it to others. From a macro perspective, it doesn’t actually know what it finds because it’s dumb in the end. We feed them intelligence, otherwise they are nothing.  

So, instead of panicking about the uncertainties that AI may bring into our lives, we should take a bow and appreciate the efforts humans gave into creating something so huge, so complex like AI.

And remember, AI has always created jobs in the past and didn’t take them. So, be hopeful!

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

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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.

 
Google’s-DeepMind
 

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

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

“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

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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.

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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

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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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Google Is All Set to Wipe Off Artificial Stupidity

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Google Is All Set to Wipe Off Artificial Stupidity

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.”

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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.

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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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More Powerful and Soon To Be Everywhere, Here’s All You Need to Know about AI

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More Powerful and Soon To Be Everywhere, Here’s All You Need to Know about AI
 

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.

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