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

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Indian-Startups-Relying-on-Artificial-Intelligence-to-Know-Their-Customers-Better

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:

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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.

olacabs-picture

“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. 

Flipkart

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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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.”

Googleplex-Google-Logo-AH-6

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.

google_io_2017_ai_1499777827549

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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Pentagon Fights Off ISIS with Machine Learning and Big Data

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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.

 
Pentagon Fights Off ISIS with Machine Learning and Big Data
 

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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