Artificial Intelligence Can Translate Languages Without a Dictionary

Artificial Intelligence Can Translate Languages Without a Dictionary

Secretary of Defense Chuck Hagel announced that his department intended to lead the coming AI revolution with what he called the “Third Offset Strategy.” For the uninitiated, the Second Offset Strategy was in response to the buildup of conventional forces along the Central European front by the Soviet Union and its satellite states … which itself was a response to the First Offset Strategy that had the U.S. putting its own forces in the field. America didn’t want to match the Soviets soldier for soldier, so new technology like long-range sensors and a new generation of guided munitions and submunitions was developed to give the U.S. an edge. Hagel wanted to approach the third iteration in much the same way, but in the last few years, new military uses of AI have been increasingly pioneered by the Russians and Chinese. Battle-ready artificial intelligence is also on the mind of NATO, which released a report Wednesday stating that NATO needs to prepare for the future of war by investing in AI. AI-driven military development doesn’t just have to be robot soldiers and armed drones, either. In the report detailing America’s risk of falling behind Russia and China, Govini, a data analytics firm that contracts with the U.S. government, describes uses for AI that can help humans make more timely and relevant combat decisions, new forms of human-machine collaboration, and new ways that neural nets can help make sense of some of the massive data sets that the military has access to. To be clear, the Department of Defense isn’t completely sleeping on AI. Spending on AI, big data, and cloud services reached $7.4 billion in the 2017 fiscal year, which is almost a third more than what was spent in 2012. The commercial sector spends far more each year than the DoD does on AI: Ford spent $1 billion to buy an AI startup in a single deal this past February, for example. On a trip to Silicon Valley earlier this year, Defense Secretary James Mattis said he sees a need to do a better job of integrating commercial AI into his department.

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A New Way for Machines to See, Taking Shape in Toronto – The … – New York Times

A New Way for Machines to See, Taking Shape in Toronto – The … – New York Times

TORONTO — In 2012, Geoffrey Hinton changed the way machines see the world.Along with two graduate students at the University of Toronto, Mr. Hinton, a professor there, built a system that could analyze thousands of photos and teach itself to identify common objects like flowers and cars with an accuracy that didn’t seem possible.He and his students soon moved to Google, and the mathematical technique that drove their system — called a neural network — spread across the tech world. They looked like something he had found at the bottom of an old toy chest.He explained that the blocks were two halves of a pyramid, and he asked if I could put the pyramid back together. All I had to do was find the two sides that matched and line them up. But I couldn’t.Most people fail this test, he told me, including two tenured professors at the Massachusetts Institute of Technology. But we all failed, Mr. Hinton explained, because the puzzle undercuts the natural way we see something like a pyramid.We do not recognize an object by looking at one side and then another and then another. And because of the way the puzzle cuts the pyramid in two, it prevents us from picturing it in 3-D space as we normally would. With his capsule networks, Mr. Hinton aims to finally give machines the same three-dimensional perspective that humans have — allowing them to recognize a coffee cup from any angle after learning what it looks like from only one. “It is a fact that is ignored by researchers in computer vision,” he said. “And that is a huge mistake.”Loosely modeled on the web of neurons in the human brain, neural networks are algorithms that can learn discrete tasks by identifying patterns in large amounts of data. By analyzing thousands of car photos, for instance, a neural network can learn to recognize a car.This mathematical idea dates back to the 1950s, but the concept has found real-world applications in recent years, thanks to improvements in processing power and the large amounts of data generated by the internet. Over the last five years, neural networks have accelerated the progress of everything from smartphone digital assistants to language translation services to autonomous robots.But these methods are still a long way from delivering machines with true intelligence — and new research is needed to deliver the kinds of autonomous machines that so many of the top tech companies are now promising, including conversational computers and driverless cars.Mr. Hinton, who is a kind of godfather figure for the A.I. community, is part of a small but increasingly vocal group of specialists who are working to push the industry into these alternative areas of research.Oren Etzioni, chief executive of the Allen Institute for Artificial Intelligence, based in Seattle, lamented what he called the industry’s myopia. Its current focus on neural networks, he said, will hurt the progress of A.I. in the long run.Eric Horvitz, who oversees much of the A.I. work at Microsoft, argued that neural networks and related techniques were small advances compared with technologies that would arrive in the years to come.“Right now, what we are doing is not a science but a kind of alchemy,” he said.Mr. If a neural network is trained on images that show a coffee cup only from a side, for example, it is unlikely to recognize a coffee cup turned upside down.Now Mr. Hinton and Sara Sabour, a young Google researcher, are exploring an alternative mathematical technique that he calls a capsule network. But Mr. Hinton believes his capsule networks can eventually expand to a wider array of situations, accelerating the progress of computer vision and things like conversational computing. Capsule networks are an attempt to mimic the brain’s network of neurons in a more complex and structured way, and he explained that this added structure could help other forms of artificial intelligence as well. The new lab is emblematic of what some believe to be the future of cutting-edge tech research: Much of it is expected to happen outside the United States in Europe, China and longtime A.I. research centers, like Toronto, that are more welcoming to immigrant researchers. Ms. Sabour is an Iranian researcher who wound up in Toronto after the United States government denied her a visa to study computer vision at the University of Washington.Her task is to turn Mr. Hinton’s conceptual idea into a mathematical reality, and the project is bearing fruit. They recently published a paper showing that in certain situations their method can more accurately recognize objects when viewing them from unfamiliar angles.“It can generalize much better than the traditional neural nets everyone is now using,” Ms. Sabour said.When I walked into his office this month, Mr. Hinton, dressed in his usual button-down shirt and sweater, handed me two large white blocks.

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An Old Technique Could Put Artificial Intelligence in Your Hearing … – WIRED

An Old Technique Could Put Artificial Intelligence in Your Hearing … – WIRED

That’s limiting, because some places AI could be useful have privacy, time, or energy constraints that mean handing off data to a distant computer is impractical.You might say Mythic’s project is an exercise in time travel. “By the time I went to college analog computers were gone,” says Eli Yablonovitch, a professor at University of California Berkeley who got his first degree in 1967. “This brings back something that had been soundly rejected." Analog circuits have long been relegated to certain niches, such as radio signal processing.Henry says internal tests indicate Mythic chips make it possible to run more powerful neural networks in a compact device than a conventional smartphone chip. "This can help deploy deep learning to billions of devices like robots, cars, drones, and phones," he says.Related StoriesHenry likes to show the difference his chips could make with a demo in which simulations of his chip and a smartphone chip marketed as tuned for AI run software that spots pedestrians in video from a camera mounted on a car. In the demo, Mythic’s chip can spot people from a greater distance, because it doesn’t have to scale down the video to process it. The suggestion is clear: you’ll be more comfortable sharing streets with autonomous vehicles that boast analog inside.Digital computers work by crunching binary numbers through clockwork-like sequences of arithmetic. Electrons flow through a maze of components like amplifiers and resistors that do the work of mathematical operations by changing the current or combining it with others. A Mythic chip can also do all the work of running a neural network without having to tap a device's memory, which can interfere with other functions. But that's not a problem for running neural networks, which are prized for their ability to make sense of noisy data like images or sound. "Analog math is great for neural networks, but I wouldn't balance my check book with it," Henry says.If analog comes back, it won't be the first aspect of the Mark 1 Perceptron to get a second life. The machine was one of the earliest examples of a neural network, but the idea was mostly out of favor until the current AI boom started in 2012.Objects identified in video by a simulation of a conventional smartphone chip tuned for artificial intelligence. The company's chips are repurposed flash memory chips like those inside a thumb drive—a hack that turns digital storage into an analog computer.The hack involves writing out the web of a neural network for a task such as processing video onto the memory chip's transistors. Those signals are converted back into digital to complete the processing and allow the chip to work inside a conventional digital device. The company will initially target the camera market, where applications include consumer gadgets, cars, and surveillance systems.Mythic hopes its raise-the-dead strategy will keep it alive in a crowded field of companies working on custom silicon for neural networks. Apple and Google have added custom silicon to power neural networks into their latest smartphones.Yablonovitch of Berkeley guesses that Mythic won't be the last company that tries to revive analog. He gave a talk this month highlighting the opportune match between analog computing and some of today's toughest, and most lucrative, computing problems.“The full potential is even bigger than deep learning,” Yablonovitch says. He says there is evidence analog computers might also help with the notorious traveling-salesman problem, which limits computers planning delivery routes, and in other areas including pharmaceuticals, and investing.Something that hasn’t changed over the decades since analog computers went out of style is engineers’ fondness for dreaming big. Rosenblatt told the New York Times in 1958 that “perceptrons might be fired to the planets as mechanical space explorers.” Henry has extra-terrestrial hopes, too, saying his chips could help satellites understand what they see. When shown examples of different shapes, it built “knowledge” using its 512 motors to turn knobs and tune its connections. "It was a major milestone," says Spicer.Computers today don’t log their experiences—or ours—using analog parts like the Perceptron’s self-turning knobs. CEO and cofounder Mike Henry says it’s necessary if we’re to get the full benefits of artificial intelligence in compact devices like phones, cameras, and hearing aids.Mythic's analog chips are designed to run artificial neural networks in small devices. Mythic uses analog chips to run artificial neural networks, or deep-learning software, which drive the recent excitement about AI. The technique requires large volumes of mathematical and memory operations that are taxing for computers—and particularly challenging for small devices with limited chips and battery power.

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Amazon’s AWS DeepLens camera wants to teach you deep learning

Amazon’s AWS DeepLens camera wants to teach you deep learning

Announced today at the AWS re:Invent 2017 conference, the $249 (£185/AU$330 converted) DeepLens video camera is designed to help train developers in deep learning programming techniques. April 14, 2018, is the projected date of availability on Amazon.com, although you can preorder today. It's what fuels Amazon's Alexa-enabled speakers, what makes them able to differentiate among various voices, and what makes facial recognition cameras able to distinguish you from your neighbor. The services range from hosting back-end site operations, to storing recorded video, (like what's offered as an optional upgrade via Amazon's Cloud Cam indoor security camera) to messaging services and even AR and VR applications.  47 46 indoor security cameras for a safer smart home Rather than functioning as a traditional indoor security camera, the AWS DeepLens is a training device. It also has microSD, Micro-HDMI and two USB ports to accommodate a variety of projects. Some of the potential projects Amazon lists include object detection, activity recognition and face detection — all fueled by Amazon SageMaker, the AWS machine learning service enabling these projects.  "Amazon SageMaker is a fully managed end-to-end machine learning service that enables data scientists, developers, and machine learning experts to quickly build, train, and host machine learning models at scale," Amazon said in a blog post. With the growth of machine learning in our phones, security cameras and other common consumer products, Amazon's AWS DeepLens video camera could help drive further innovation.

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2 Berkeley grads are using AI to make stock-buying decisions — and it could change investing forever

2 Berkeley grads are using AI to make stock-buying decisions — and it could change investing forever

EquBot / Art Amador Equbot's AI Powered Equity ETF uses IBM's Watson technology to construct a stock portfolio, employing machine learning to make rational investment decisions. The original idea for the fund was synthesized in a classroom at UC-Berkeley, where founders Chida Khatua and Art Amador met during an entrepreneurship class. As it turned out, Khatua had been researching for years how to sift through massive amounts of data in a way that extended far beyond human capabilities. "His background — in artificial intelligence and machine learning — was the perfect use case," Amador says. "We started talking about how that could apply to the equity markets." Even though the early groundwork had been laid for what would eventually become their newest venture, Khatua and Amador went their separate ways after the program ended. But the gears in Khatua's head never stopped turning, and in September 2016 he invited Amador to join him in building a product that would combine their respective areas of expertise. Amador took some time to think about it. In his mind, the result would be an AI-powered quantitative hedge fund, and he wasn't sure if he wanted to give up his job at Fidelity for that. And if he combined it with Amador's investment prowess, they could build an ETF available to be traded by the average person with a brokerage account. Khatua's background in AI and machine learning complemented Amador's history in private wealth management, and the duo decided to launch an exchange-traded fund. Equbot "Working at Intel gave me insight into how machine learnings and AI technology is maturing and how the benefits it offers can really be maximized," Khatua tells Business Insider. "It gave me a unique perspective, and I asked myself for a while when the right time would be to go out and create some product that can help many people." A big part of Amador's decision to ultimately join Khatua in pursuing an ETF was the latter's acceptance into the highest tier of the IBM Global Entrepreneurship Program. That gave Khatua $125,000 with which to pursue his idea, and it provided Amador crucial validation for the endeavor. The eventual result was the recently launched AI Powered Equity ETF (ticker: AIEQ), which analyzes more data than humanly possible, all in the pursuit of building the perfect portfolio of 30 to 70 stocks. In addition to analyzing regulatory filings, quarterly news releases, articles, social-media postings, and management teams, it's also designed to assess market sentiment and weed out potentially faulty inputs — including so-called fake news. "A rational investor looks at a company as a whole and they draw insight into what’s right looking at the complete picture," Khatua says. "The AI model helps us do that. That's a key element of AIEQ and one that sets it apart from the hedge funds that use AI to construct trading strategies. Khatua says many of those models function as a "conceptual black box," because the presence of certain stocks can't be explained in a rational way. In his mind, Equbot's ETF offers the best of both worlds: It's based on a mountain of analysis and the stock-picking methodology can be explained. Whether it related to global markets, macroeconomic factors, specific companies, or full sectors, their curiosities were wide ranging — and Amador wondered if he'd ever find a way to be the all-knowing oracle they desired. Amador points out that even if a firm had 6,000 analysts each responsible for reading 150 to 200 articles about one stock each day, that work would have to be cross-referenced against the findings of all other employees, then funneled into one objective opinion. The biggest laggards in the fund are Lifepoint Health, Newell Brands and Vista Outdoor, which have each dropped more than 20% over the period. That all changed one day in the fall of 2014 when Amador was pursuing his MBA at the Haas School of Business at the University of California at Berkeley. The way that Khatua and Amador see it, interest in their product will continue to grow as long as personal bias continues to cloud investment decisions — something they see happening even at the highest level of professional money management. As part of an entrepreneurship class, he was placed in the same cohort as a long-time Intel engineer and machine-learning specialist named Chida Khatua, and the two got to talking. That conversation led to what its creators say is the world's first AI-powered exchange-traded fund, one built on technology that could change the paradigm for how computers are used to invest. The fund — powered by IBM's Watson supercomputing technology — didn't end up launching for a few more years, but its roots can be traced back to that fateful first conversation at Berkeley. "I was telling him it was impossible to have infinite knowledge about every stock, and about everything going on in markets," he tells Business Insider. "I told him that there's simply too much information out there and not enough time to distill it into actionable ideas."

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AWS Announces Five New Machine Learning Services and the World’s First Deep Learning-Enabled Video Camera for Developers

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The U.S. Risks Falling Behind Russia and China in Its Use of AI in … – MIT Technology Review

The U.S. Risks Falling Behind Russia and China in Its Use of AI in … – MIT Technology Review

Secretary of Defense Chuck Hagel announced that his department intended to lead the coming AI revolution with what he called the “Third Offset Strategy.” For the uninitiated, the Second Offset Strategy was in response to the buildup of conventional forces along the Central European front by the Soviet Union and its satellite states … which itself was a response to the First Offset Strategy that had the U.S. putting its own forces in the field. America didn’t want to match the Soviets soldier for soldier, so new technology like long-range sensors and a new generation of guided munitions and submunitions was developed to give the U.S. an edge. Hagel wanted to approach the third iteration in much the same way, but in the last few years, new military uses of AI have been increasingly pioneered by the Russians and Chinese. Battle-ready artificial intelligence is also on the mind of NATO, which released a report Wednesday stating that NATO needs to prepare for the future of war by investing in AI. AI-driven military development doesn’t just have to be robot soldiers and armed drones, either. In the report detailing America’s risk of falling behind Russia and China, Govini, a data analytics firm that contracts with the U.S. government, describes uses for AI that can help humans make more timely and relevant combat decisions, new forms of human-machine collaboration, and new ways that neural nets can help make sense of some of the massive data sets that the military has access to. To be clear, the Department of Defense isn’t completely sleeping on AI. Spending on AI, big data, and cloud services reached $7.4 billion in the 2017 fiscal year, which is almost a third more than what was spent in 2012. The commercial sector spends far more each year than the DoD does on AI: Ford spent $1 billion to buy an AI startup in a single deal this past February, for example. On a trip to Silicon Valley earlier this year, Defense Secretary James Mattis said he sees a need to do a better job of integrating commercial AI into his department.

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Introduction to Blockchain and What It Means for Big Data

Introduction to Blockchain and What It Means for Big Data

According to this article, “Arguably the most significant development in information technology over the past few years, blockchain has the potential to change the way that the world approaches big data, with enhanced security and data quality just two of the benefits afforded to businesses using Satoshi Nakamoto’s landmark technology.” What Is a Blockchain? Oliver Bussmann, CIO of UBS, says that blockchain technology could “pare transaction processing time from days to minutes.” The business imperative in financial services for blockchain is powerful. Huge data lakes of blocks that contain the full history of every financial transaction, all available for analysis. Blockchain provides for the integrity of the ledger, but not for the analysis. Opportunities for Big Data Analytics Recently, a consortium of 47 Japanese banks signed up with a blockchain startup called Ripple to facilitate money transfers between bank accounts using blockchain. One of the reasons traditional real-time transfers were expensive was because of the potential risk factors. Double-spending (which is a form of transaction failure where the same security token gets used twice) is a real problem with real-time transfers. Each record in the database is called a block and contains details such as the transaction timestamp as well as a link to the previous block. With blockchain, that risk is largely avoided. Big data analytics makes it possible to identify patterns in consumer spending and identify risky transactions a lot more quickly than they can be done currently. Across healthcare, retail, and public administration, establishments have started experimenting with blockchain to handle data to prevent hacking and data leaks. This can help prevent a repeat of events such as the 2015 attacks that led to the theft of over 100 million patient records. Since the blockchain has a database record for every single transaction, it provides a way for institutions to mine for patterns in real-time, if need be. But all of these possibilities also raise questions about privacy. This is in direct contradiction to the reason why blockchain and Bitcoin became popular in the first place. Several industry experts have expressed concerns that a technology that can provide a record of every transaction can be exploited for everything “from customer profiling to other less benign reasons.” From another perspective, however, blockchain greatly improves transparency in data analytics. Uncovering Transactional Data The data within the blockchain is predicted to be worth trillions of dollars as it continues to make its way into banking, micropayments, remittances, and other financial services. In fact, the blockchain ledger could be worth up to 20% of the total big data market by 2030, producing up to $100 billion in annual revenue. Data intelligence services are emerging to help financial institutions, governments, and all kinds of organizations delve into who they might be interacting with on the blockchain and uncover “hidden” patterns. Uncovering Social Data As the popularity of bitcoin advanced in 2014 and 2015, the virtual currency began to fluctuate heavily as a result of real-world events and the general public’s sentiment about the technology. Also, due to the fact that the same transaction is recorded over multiple distributed database systems, the technology is secure by design. According to Rick Burgess of Freshminds: “Using social data to predict consumer behavior is nothing new, and many traders have been looking to include social metrics into their trading algorithms. However, because there are so many factors involved in pricing most financial instruments, it can be extremely difficult to predict how markets will change.” Fortunately, bitcoin and social media users tend to align quite well, and it may be beneficial to use them both for data analysis, as he further explains: Bitcoin users tend to be in the same demographic as social media users, so their attitudes, opinions, and sentiments towards bitcoin are well documented. The value of bitcoins and other cryptocurrencies are determined almost solely by market demand because the number of coins on the market is predictable and are not tied to any physical goods. Uncovering New Forms of Data Monetization According to Bill Schmarzo, CTO of Dell EMC Services, blockchain technology also “has the potential to democratize the sharing and monetization of data and analytics by removing the middleman from facilitating transactions.” In the business world, this gives consumers stronger negotiating powers over companies. With the above in mind, blockchain is immutable — information remains in the same state for as long as the network exists. Schmarzo also explains how the blockchain may lead to new forms of data monetization because it has the following big data ramifications: All parties involved in a transaction have access to the same data. This accelerates data acquisition, sharing, the quality of data and data analytics. This provides a complete overview of a transaction from start to finish, eliminating the needs for multiple systems. Ultimately, the blockchain could become a key enabler of data monetization by creating new marketplaces where companies and individuals can share, sell, and offer their data and analytical insights directly with each other. Spearheaded by the large-scale adoption of bitcoin, blockchain technologies are gaining ground throughout the business and financial worlds. Blockchain and Big Data When you talk about blockchain in the context of Bitcoin, the connection to big data seems a little tenuous.

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Red Velvet say they don’t have SME founder Lee Soo Man’s number

Red Velvet say they don’t have SME founder Lee Soo Man’s number

allkpop.com TRENDING A security video of Girls' Generation Taeyeon's 3-car collision has been aired on news channels.  On November 28 at around 8PM KST, Taeyeon's Mercede… The voting for '2017 MAMA' has drawn to a close. This year, '2017 MAMA' will be held for a total of 4 days in 3 different locations; Vietnam, Japan, a… Taeyeon refuted claims that she had not apologized.A person claiming to be another victim of the car accident said that Taeyeon had not apologized…

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Meet Faheem Mumtaz, the Man Behind Pakistan’s Largest Job Portal – PaperPK

Meet Faheem Mumtaz, the Man Behind Pakistan’s Largest Job Portal – PaperPK

We get around 75,000 to 120,000 sessions per day and over the past year, we’ve averaged around 80,000 sessions per day.Our users aren’t only limited to cities and we get a lot of interest from smaller cities as well.On Alexa, we are still the top jobs portal in Pakistan. But the success of our portal over time without any proper and concrete marketing speaks for itself.Is PaperPK a profitable venture?We are turning a considerable profit with a workforce of 15 full time workers.How do you monetize your platform?We are monetizing the platform by using the portal as source of providing services and third party tools such as Google Adsense. We also utilize banner ads and commercial direct ads for various companies whose target market are the young unemployed individuals, job seekers and students.Are there going to be any major changes in UI, UX and any added features?We continuously evolve our design and add new features and functions to PaperPK. Our interface and navigation is not as complex as other job portals and we use a three click approach so that our users can access all the information instantly.In addition to metropolitan cities, we keep in mind the job seekers of small cities as well. We focus on jobs in both the private and government sectors and list all job opportunities whether they are white collar or blue collar so that we can cater to the needs of all kinds of job seekers.With 3G/4G subscribers growing in Pakistan, do you think it has contributed to your platform’s success? We discussed his journey, PaperPK and various dynamics of the job market in Pakistan.Tell us a little bit about yourself, your early years, and professional career.I graduated from Punjab University Lahore and did my Masters with major in E-Commerce, which was still in its infancy in the 2000s. We’ll be launching a mobile app to provide a better experience to our mobile users soon.Based on your claim that PaperPK has crossed Rozee in daily traffic, can you be a bit more specific? Rozee was considered as a pioneer and torch bearer in the field of online job portals. We want to be the most useful website tool for employers to hire the right talent time and in a more efficient way.A word of advice for Pakistani entrepreneurs out there? In order to achieve accomplishment in any field you need to show dedication. Change is the spice of life and as long as you are adding value to the market with your business, no one can beat you.One thing you must keep in mind is that your budget is an important factor but it is not the key to success. The real reason behind success is how you communicate with your target market and how fast you respond to the requirements of it.When I started my website with limited resources ,it was just a single person’s venture.I was focused and determined in my goal to make PaperPk into one of the most popular jobs website in Pakistan. I started my career as an Internee with a software house based in Islamabad named “Netsolace”, which is now known as “Broad Peak”, and was being run by Mr. Tariq Farid as its CEO, who is also behind Edible Arrangements, a highly successful venture in the USA.During those early days, I was guided and instructed by Mr. Tariq Farid as he taught me the basics of business startups over the internet and how to succeed in the online transactional environment.After some time, I realized that I am not a job oriented person and wanted to start my own business. I founded PaperPK, which initially had a small office-based setup with five to ten people.The turning point in my entrepreneurial career was in 2015 when PaperPK was incubated in LUMS Centre of Entrepreneurship and I met Mr. Khurram Zafar who opened my eyes to the opportunities available online.At that point, I started to adopt a competitive mindset in my business and by the grace of Allah within a year we are amongst the top in our field.What motivated you to start PaperPk?I was inspired to start an online Job Portal because such a solution didn’t exist back in 2007.Most of the individuals after completing their education scan various newspapers so that they can find the job opportunities that are available according to their education and qualification. Scanning hundreds of jobs in various newspapers is a really difficult and time consuming process.I realized this and started by collecting and filtering all the jobs that are published in newspapers and scanned and posted them on the website so that job seekers could select and filter the jobs they wanted.The idea was really innovative during those early days and got a lot of traction with job seekers. We found out that they prefer visiting a website for available jobs and vacancies instead of buying different kinds of newspapers.Who is your closest competitor? Most of our traffic comes from Punjab and our biggest competitor there is Rozee.pk, which has a huge budget, a highly qualified team and an established structure. Aside from Rozee, all the other job portals are just copying the basic structure and idea of PaperPk so we are not bothered by them.Your thoughts on Rozee?The overall structure of Rozee is huge and they are spot-on in terms of their efforts. I respect the efforts that Rozee put in capturing the Pakistani jobs market. We had about 10 to 15 employees and lacked resources at the beginning.Can you give us some hard stats regarding your daily traffic and your user base?There are no hard and fast rules when it comes to internet traffic. However, we can provide you with a generic estimate of what we experience.

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