Category Archives: Artificial Intelligence

It might be considered a platitude, but people are always looking for new ways to break away from the monotonous beat of everyday normalcy either temporarily or permanently. According to a 2013 report on drug abuse by the United States government, 9.4 percent (around 24.6 million people) of individuals age 12 or old noted that they had recreationally used a drug within the past month. This tendency to seek life-changing experiences is true whether it concerns things like the countercultural movements of the 1970s which infamously involved controversial music and use of illicit drugs or the technological experiences today.

Most people are fascinated with those experiences that allow them to escape crushing boredom and constancy of regular life. Thats why the prospect of virtual realities and the possibilities of automation afforded by artificial intelligence are so exciting. Here are some of the biggest changes related to these two fields that are quickly arriving with the technological advents of modern society.

In order to understand the importance of the changes that are currently taking place in the field of AI, a brief description of historical approaches to the problem of replicating intelligence is helpful. Lets illustrate these approaches by taking a look at how chess engines function. With regard to chess engines, the goal is clearly defined and the problem is how can we code a machine to make accurate decisions that will lead to a winning outcome despite the difficulty of running large sequence searches through possible move sequences.

In the past, engineers solved this issue through cruder methods that involved the use of decision trees and using certain mathematical methods to guide the chess engines choice and calculation of the best possible move sequences. The issue with this method and the challenge that impacts most AI development efforts is that there needs to be significant amounts of training material in order for the engine to develop sufficient resolution and accuracy in making its choices.

Another limitation that is implicit in these older methods in artificial intelligence is that the methods themselves are static there is no way for the methods to refine themselves without the help of human ingenuity. The concept of machine learning is part of the set of revolutionary methods in artificial intelligence that is addressing this limitation and attempting to surpass it.

So, where does virtual reality come into all of this? Well, to start off virtual reality is similar to artificial intelligence in the sense that the field is still in its development stages. However, virtual reality is in an even earlier stage of nascency.

With the introduction of the popular Oculus Rift to the market, the general population has gotten its first preliminary taste and involvement in virtual reality. Yet, it is apparent the methods for providing a truly fulfilling virtual reality experience are still very rough around the edges with the introduction of hamstrung attempts like Samsungs Gear VR, which is really just you attaching your phones display to your face.

Further along the path of VR development lies the innovative company Guru which aims to advance the integration of VR for exhibits and museums. A key belief of Guru is that the right technology can enhance static works of art, , and Gurus augmented reality platform seeks to bring static art like paintings of historical figures and locations to life. You will feel as if you have been literally transported into a painting as Gurus digitization software intelligently animates the canvas.

What makes Guru possible derives from its blending of the concepts of artificial intelligence and virtual reality. Artificial intelligence is used by Guru to identify major themes in a painting and distinguish between buildings, people, and objects in order to bring them to life. Meanwhile, the design of the platform exists as virtual reality, allowing visitors to easily and intuitively access it.

Recently, the allure and wonder of the culture and history associated with famous artistic works has been lost to the massive leaps in technology. The expectations of the general population have gradually increased with the subtle introduction of these technologies which have become commonplace in the lives of many people. Mythology flourished during the time of the ancient Greeks because of the uncertainty associated with the unexplored areas of nature there could always be the stray nymph running around in a vast forest. But with the certainty provided with technology advancement, that feeling of wonder at the unknown has become rarer over time. Guru allows museums to take that same leap forward in order to connect with their visitors in a manner befitting these technological advents. It amazes and stuns visitors to see this blend of technology and human ingenuity in the palms of their hands. Guru restores to art what technology has replaced our imagination.

Moving away from immersive virtual reality experiences, there are arguably virtual realities that involve the inverse the projection of the virtual into the real. Gatebox and its virtual assistant that can engage you in conversation and control the settings of your home to an extent, based on your preferences, is a good demonstration of pioneering for this specific field. One day, the machine learning methods of artificial intelligence may even be incorporated into the conversational abilities of these assistants to give them an increasingly human-like presence.

Artificial intelligence has experienced a paradigm shift in recent times. This is because the older models of decision making that involve brute force methods or decision-making trees are transitioning over to models that involve the use of neural networks instead. Artificial intelligence methods that incorporate neural networks lead to more precise decision making because they have a number of variable sensors that all go into making a decision much like how a certain proportion of neurons fire in the human brain in response to a situation. This has allowed some programs to perform more complex tasks like the precise identification of human faces.

Another relevant aspect of this shift is how artificial intelligence derives from the application of machine learning. Before, games such chess with relatively fewer calculations required were easily conquered after some decades by chess engines. However, games involving more practice and intuition such as Go have long eluded mastery by machines until Googles DeepMind AI AlphaGo was introduced.

In a surprising turnaround, Googles AI was able to beat one of the leading Go champions, Lee Sedol 4-1 in an exhibition of five games, showing the proficiency and capabilities of these new machine learning methods. Interestingly, Google has also employed these machine learning methods to work with other applications such as in the regulation of its cooling systems to be more efficient.

This post is part of our contributor series. The views expressed are the author's own and not necessarily shared by TNW.

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How virtual reality and artificial intelligence are changing life experiences - TNW

Intelligent systems may play the key to success through digital transformation and delivering on consumer expectations, but this will only be realised through intelligent deployment and management models

The predictions for AI use cases have been prolific and wide-ranging in recent years. From humanoid robots to predictive analytics for legal institutions, hedge funds, and more, there has rarely been more excitement generated by a technology than the current buzz emanating from AI software.

Application of this technology extends to mobile and telecommunications too. Here, it has become an important next step in helping operators transition from Communications Service Providers into more advanced Digital Service Providers that can predict their customers wants and needs.

AI is empowering service providers with a range of new capabilities such as deep learning, natural language processing, and cognitive computing to create a digital interface that will essentially deal directly with human beings, addressing and resolving customer service issues. Sound like science fiction, but its the new reality. AI is a huge catalyst for change, not just in telecoms, but almost every walk of life.

>See also:Making business smarter: 3 misconceptions about AI

So, what exactly is artificial intelligence? Is it about creating robots powered by super computers, which outperform their human counterparts? Or is it grounded in less futuristic, albeit still important, applications and in more sedate data crunching and algorithms than walking-and-talking machines? Its all of the above and more.

AI can be about simulating human intelligence, incorporating traits such as reasoning, perception, problem solving and forward planning. At its crux, though, AI is about the development and enactment of methods of transforming vast amounts of complex, often unstructured data into intelligent insights.

The key elements of artificial intelligence machine learning, cognitive computing, natural language processing, and sentiment analysis, combined with more effective real-time data management make this possible.

For example, rather than the time consuming and, due to human error, often inaccurate process of manually sifting through data and drawing conclusions, AI can rapidly automate processes.

It can establish rules and use algorithms to deliver accurate analytics and predictions. Importantly, this may also reveal hidden insights in data that would have been missed if the process were to be conducted by a human.

>See also:To err is human, so why not use an AI?

In turn, for service providers across all industries, this makes it possible to take much more informed actions as a result of predicting what customers will need and when, making timely, relevant, and attractive offers to drive sales and further engagement.

Findings from data can be utilised to improve services, develop new tools and technologies, and drive production or business efficiencies, in addition to wide-ranging benefits we are yet to realise. Although the terms are often used interchangeably or confused this more specific application of AI can be more closely defined as machine learning.

Much has been made of the opportunities AI can deliver to businesses and consumers, but the technology has attracted some negative publicity, particularly in relation to the potential impact of AI and robotics on the job market.

However, as with other technological and industrial revolutions which have come before it, the disappearance of jobs in some sectors coupled with the introduction of new technologies will likely spur the creation of new (human) roles in other areas of business.

New training and skills will be needed for workforces in order to adapt their jobs to the new opportunities AI presents, requiring new educators. AI technology will need maintaining and new systems will need to be developed, necessitating individuals with knowledge and experience in this new field. In addition, the automation of traditionally repetitive, administrative office jobs would arguably allow for more creativity and boost workforce morale, an advantage for any industry.

Questions over how to regulate and control AI have also become a key topic, particularly after Facebook was forced to shut down an artificial intelligence program after it created its own language.

In this instance, two bots created a series of code words and nonsensical text strings for communicating tasks. Although more efficient than a full English sentence, the phrases could not be interpreted by human controllers.

There is not enough evidence to suggest that this unforeseen development poses a threat, but its certainly a development that needs closer monitoring and regulation for the future of this technology.

>See also:How Tesco is using AI to gain customer insight

The ethics of AI and the role it will play in our lives continues to drive debate as a result. It has even split the opinions of two of Silicon Valleys most esteemed CEOs. Recently, Facebooks Mark Zuckerberg and Teslas Elon Musk found themselves embroiled in a very public row about the viability of AI, the benefits it can offer and the potential challenges it poses.

Zuckerberg was more optimistic focusing on the breakthroughs that have been made in healthcare and the development of self-driving cars. Its no surprise Zuckerberg holds this view given that Facebook has invested so heavily in AI. And, despite their differences, both CEOs have accepted AI will play a crucial role in improving their businesses.

Despite significant developments in AI over the past decade, full automation and computer super intelligence is still a while off yet. Where the technology has been making significant inroads, though, is in communications.

In the digital-first landscape of today, consumers are more demanding than ever. They expect always-on digital services and for engagement with service providers to be immediate, reliable, and on their terms through platforms of their choosing.

Until recently this engagement meant contacting a call centre, with delays and queues to connect to an operator. AI, and chatbots in particular, have revolutionised this space. Gartner recently predicted that by 2020, 85% of all customer interactions will be handled without a human agent.

Facebook launched its chatbot creation tools in 2016, allowing platform creators to build their own version of the technology and integrate these customer service tools into their business offering.

As of January 2017, a reported 45,000 developers were using Facebooks Wit.ai chatbot-building tool to create chatbots for Facebook Messenger.

>See also:The role of artificial intelligence in cyber security

In addition to driving customer satisfaction through quicker, more convenient interaction with companies, the use of chatbots can also prove a revenue-booster: an estimated 36% of sales representative positions in the US could be automated, meaning annual savings from salaries of at least $15 billion. However, it is not only in communications generally where AI is proving its worth, but in the telecommunications industry more specifically.

With the rise of disruptive new digital service providers (DSPs) and the continuing shift to data usage, mobile operators and communication service providers (CSPs) face threats to their traditional revenue streams.

Access to data services is now seen by consumers and businesses as a necessity, and, as the market has opened up to more competition and more choice consumers are demanding more from their service providers.

Although not solving all ills, AI can be seen as a force of differentiation that will empower service providers to drive value across their businesses. And not only in the customer interaction space but also in areas like network management and optimisation, and improving subscriber experiences through more accurate data visibility and analytics.

It is within this latter area that AI will play an essential role, ensuring telcos not only survive but thrive. The imminent arrival of next generation 5G technologies and networks, coupled with the rapidly expanding IoT will vastly increase the number of end-points a CSP/DSP must manage.

More consumer connected devices, a greater number of machine-to-machine (M2M) connections, and sensors embedded in infrastructure and vehicles will result in a dizzying amount of communication between technologies and information traversing networks.

This level of data is unmanageable for the human to process, yet an AI system can provide real time visibility and management of this information, delivering intelligent data analytics to improve processes.

AI can also extract data from one part of a business in order to place it in another, linking up sectors to allow them to learn from each other.

In the case of data from consumer devices, AI-grounded analysis can also be used to improve services for subscribers. Customer behaviour and engagement data can be gathered and processed more rapidly and in greater volumes, used to influence the development of optimal pricing models and create new services.

>See also:NHS Trust successfully fought back WannaCry ransomware with AI

The granular data insight garnered by AI goes beyond that capable of humans, meaning a deeper understanding and learning of competition data, such as BSS information, advertising and voice of the consumer type feedback.

AI will not just allow for reactive management of customer services, but, due to the predictive capabilities of the technology, itll also support proactive customer care. Implementing intelligence and automation will enable operators to anticipate the needs of their customers in order to engage with them via the channel of their choosing and at the time most suited to their preferences. For traditional CSPs looking to revitalise their business in order to keep up with innovative DSPs, this kind of action could prove a vital differentiator.

Artificial intelligence will also deliver huge benefits to network management. Many telcos have already introduced network functions virtualisation (NFV) and software defined networking (SDN), as well as moving processes and applications to the cloud. Artificial intelligence can be harnessed to aid efficient traffic routing, as well as managing network traffic capacity. Faults on an operators network can quickly be identified, and problems rectified.

Data on factors such as capacity demands and user behaviour can be analysed and networks can be automatically configured in response. Again, the use of AI technologies means these processes will be proactive rather than reactive.

Machine learning systems can be taught to recognise patterns in data and information, and networks and applications can be adjusted and altered in order to solve any problems before they impact the consumer.

Widespread implementation of such systems is a while off yet, although the aforementioned chatbots are making strides when it comes to CSPs and their customer service offerings.

>See also:AIs impact on customer experience

Indeed, many CSPs are ready for a full transformation towards AI implementations, whilst others are taking it at a slower pace. This said, in addition to developing and fine tuning AI systems, an operator or any business considering implementing AI must recognise the level of risk associated with this move, and ensure they implement strategies and business models to maximise opportunity whilst reducing risk.

Artificial intelligence can help boost efficiencies, aid customer engagement and services, and drive revenue as a result. However, any potential financial gain must be weighed against the cost of investing in an AI strategy.

This cost includes not only the AI tech itself, but the training which will be needed to ensure that workforces are trained to support implementation, and any new skills required added to the business through new hires.

This may include change management committees, which can help to manage an AI implementation process, including overseeing the cultural changes which are often experienced when a business makes such a dramatic move.

Finally, many traditional CSPs will likely have already experienced challenges caused by digital transformation in the industry. Building an AI system in-house could easily exacerbate any problems, so companies wishing to make this move should consider looking to external suppliers to instead create and help deploy any new system.

Even after 60-plus years, artificial intelligence is still a while off peak maturity. Equally, the implementation by any business of an AI strategy should not be a rushed process. The benefits artificial intelligence can deliver will be great, but these must always be costed against potential pitfalls.

Intelligent systems may play the key to success through digital transformation and delivering on consumer expectations, but this will only be realised through intelligent deployment and management models.

Sourced byJonathan Kaftzan, head of Digital & Intelligence marketing at Amdocs

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Artificial intelligence is transforming the enterprise - Information Age

Updated August 07, 2017 06:08:12

Artificial intelligence has jumped from sci-fi movie plots into mainstream news headlines in just a couple of years.

And the headlines are often contradictory. AI is either a technological leap into greater prosperity or mass unemployment; it will either be our most valuable servant or terrifying master.

But what is AI, how does it work, and what are the benefits and the concerns?

AI is a computer system that can do tasks that humans need intelligence to do.

"An intelligent computer system could be as simple as a program that plays chess or as complex as a driverless car," Mary-Anne Williams, professor of social robotics at the University of Technology, Sydney, said.

A driverless car, for example, relies on multiple sensors to understand where it is and what's around it. These include speed, location, direction and 360-degree vision. Based on those inputs, among others, the "intelligent" computer system controls the car by deciding, like a human would, when to turn the steering and when to accelerate or brake.

Then there's machine learning, a subset of AI, which involves teaching computer programs to learn by finding patterns in data. The more data, the more the computer system improves.

"Whether it's recognizing objects, identifying people in photos, reading lung scans or transcribing spoken mandarin, if we pick a narrow task like that [and] we give it enough data, the computer learns to do it as well as, if not better, than us," University of New South Wales professor of artificial intelligence Toby Walsh said.

AI doesn't have to sleep or make the same mistake twice. It can also access vast troves of digital data in seconds. Our brains cannot.

Yes, probably every day.

AI is in your smart phone; it's there every time you ask a question of iPhone's Siri or Amazon's Alexa. It's in your satellite navigation system and instant translation apps.

AI algorithms recognise your speech, provide search results, help sort your emails and recommend what you should buy, watch or read.

"AI is the new electricity," according to Andrew Ng, former chief scientist at Baidu, one of the leading Chinese web services companies. AI will increasingly be all around you from your phone to your TV, car and home appliances.

Four factors have now converged to push AI beyond games and into our everyday lives and workplaces:

The term artificial intelligence was first coined in 1956 by US computer scientist John McCarthy. Until recently, the public mostly heard about AI in Hollywood movies like The Terminator or whenever it defeated a human in a competition.

In 1997, IBM's Deep Blue computer beat Russian chess master Garry Kasparov. In 2011, IBM's supercomputer Watson beat human players on the US game show Jeopardy. Last year, Google's AlphaGo beat Go master Lee Sedol.

"We now have the compute power, the data, the algorithms and a lot of people working on the problems," Professor Walsh said.

AI promises spectacular benefits for humanity, including better and more precise medical diagnosis and treatment; relieving the drudgery and danger of repetitive and dehumanising jobs; and super-charging decision making and problem solving.

"Driverless cars could save many, many lives because 95 per cent of accidents are due to human error," Professor Walsh said.

"Many of the problems that are stressing our planet today will be tackled through having better decision making with computers" that access and analyse vast troves of data, he said.

There are a range of concerns:

Experts are famously split on this.

Prominent tech entrepreneurs and scientists such as Elon Musk and Stephen Hawking, among others, warn that AI could reach and quickly surpass humans, transforming into super-intelligence that would render us the second most intelligent species on the planet.

Musk has compared it to "summoning the demon". Scientists call it singularity, "where machines improve themselves almost without end," Professor Walsh said.

Facebook's Mark Zuckerberg accuses Musk of being alarmist. Professor Walsh says we don't yet even fully understand all the facets of human intelligence and there may be limits to how far AI can develop.

He's surveyed 300 of his AI colleagues around the world and most believe if AI can reach human level intelligence, it is at least 50 to 100 years away.

If it happens, humanity will likely have already solved most of the problems about whether the machines' values are aligned with ours. "I'm not so worried about that," he says.

The recent push into AI came from big US tech companies such as Google, Facebook, Amazon, Microsoft and Apple. And the US military. What could go wrong?

There's growing concern that these companies are too big and control too much data, which trains the AI algorithms.

China has now also joined the race with plans to dominate the world in AI development by 2030.

There's presently very little national or international regulation around how AI is developed. The Big Tech companies have begun discussing the need for guiding principles to ensure AI is only used for public good.

"One of those is what is the point of AI? It has to be to augment people, to support people, not replace them," Microsoft Australia national technology officer James Kavanagh says.

"Secondly, it has to be democratised. It can't be in the hands of a small number of technology companies.

"Thirdly, it has to be built on foundations of trust. We need to be able to understand any biases in algorithms and how they make decisions."

Topics: robots-and-artificial-intelligence, science-and-technology, australia

First posted August 07, 2017 06:02:12

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Explainer: What is artificial intelligence? - ABC Online

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Artificial Intelligence News & Articles - IEEE Spectrum

Facebook has been working on artificial intelligence that claims to be great at negotiating, makes up its own language and learns to lie.

OMG! Facebook must be building an AI Trump! Art of the deal. Biggest crowd ever. Cofveve. Beep-beep!

This AI experiment comes out of a lab called Facebook Artificial Intelligence Research. It recently announced breakthrough chatbot software that can ruthlessly negotiate with other software or directly with humans. Research like that usually gets about as much media attention as a high school math bee, but the FAIR project points toward a bunch of intriguing near-term possibilities for AI while raising some creepy concernslike whether it will be kosher for a bot to pretend it is human once bots get so good you cant tell whether theyre code or carbon.

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AI researchers around the world have been working on many of the complex aspects of negotiation because it is so important to technologys future. One of the long-held dreams for AI, for example, is that well all have personal bot-agents we can send out into the internet to do stuff for us, like make travel reservations or find a good plumber. Nobody wants a passive agent that pays retail. You want a deal. Which means you want a badass bot.

There are so many people working on negotiating AI bots that they even have their own Olympicsthe Eighth International Automated Negotiating Agents Competition gets underway in mid-August in Melbourne, Australia. One of the goals is to encourage design of practical negotiation agents that can proficiently negotiate against unknown opponents in a variety of circumstances. One of the leagues in the competition is a Diplomacy Strategy Game. AI programmers are anticipating the day when our bot wrangles with Kim Jong Uns bot over the fate of the planet while Secretary of State Rex Tillerson is out cruising D.C. on his Harley.

Artifical Intelligence is no longer a futuristic concept. Bots can already debate, negotiateand lielike humans. Isaac Lawrence/AFP/Getty

As the Facebook researchers point out, todays bots can manage short exchanges with humans and simple tasks like booking a restaurant, but they arent able to have a nuanced give-and-take that arrives at an agreed-upon outcome. To do that, AI bots have to do what we do: make a mental model of the opponent, anticipate reactions, read between the lines, communicate in fluent human language and even throw in a few bluffs. Facebooks AI had to figure out how to do those things on its own: The researchers wrote machine-learning software, then let it practice on both humans and other bots, constantly improving its methods.

This is where things got a little weird. First of all, most of the humans in the practice sessions didnt know they were chatting with bots. So the day of identity confusion between bots and people is already here. And then the bots started getting better deals as often as the human negotiators. To do that, the bots learned to lie. This behavior was not programmed by the researchers, Facebook wrote in a blog post, but was discovered by the bot as a method for trying to achieve its goals. Such a trait could get ugly, unless future bots are programmed with a moral compass.

The bots ran afoul of their Facebook overlords when they started to make up their own language to do things faster, not unlike the way football players have shorthand names for certain plays instead of taking the time in the huddle to describe where everyone should run. Its not unusual for bots to make up a lingo that humans cant comprehend, though it does stir worries that these things might gossip about us behind our back. Facebook altered the code to make the bots stick to plain English. Our interest was having bots who could talk to people, one of the researchers explained.

The bots ran afoul of their Facebook overlords when they started to make up their own language to do things faster. Dado Ruvic/Reuters

Outside of Facebook, other researchers have been working to help bots comprehend human emotions, another important factor in negotiations. If youre trying to sell a house, you want to model whether the prospective buyer has become emotionally attached to the place so you can crank up the price. Rosalind Picard of the Massachusetts Institute of Technology has been one of the leaders in this kind of research, which she calls affective computing. She even started a company, Affectiva, thats training AI software in emotions by tracking peoples facial expressions and physiological responses. It has been used to help advertisers know how people are reacting to their commercials. One Russian company, Tselina Data Lab, has been working on emotion-reading software that can detect when humans are lying, potentially giving bot negotiators an even bigger advantage. Imagine a bot that knows when youre lying, but youll never know when it is lying.

While many applications of negotiating botslike those personal-assistant AI agentssound helpful, some seem like nightmares. For instance, a handful of companies are working on debt-collection bots. Describing his companys product, Ohad Samet, CEO of debt-collection AI maker TrueAccord, told American Banker , People in debt are scared, theyre angry, but sometimes they need to be told, Look, this is the debt and this is the situation, we need to solve this. Sometimes being too empathetic is not in the consumers best interest. It sounds like his bots are going to negotiate by saying, Pay up, plus 25 percent compounded daily, or we make you part of a concrete bridge strut.

Put all of these negotiation-bot attributes together and you get a potential monster: a bot that can cut deals with no empathy for people, says whatever it takes to get what it wants, hacks language so no one is sure what its communicating and cant be distinguished from a human being. If were not careful, a bot like that could rule the world.

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How Facebook's AI Bots Learned Their Own Language and How to Lie - Newsweek

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Ever wanted to dress up like a celebrity? Or maybe you wanted to own attire, which you recently saw someone wearing but didnt know where to get it from? Artificial Intelligence is the technology you need. This tool identifies your clothes and finds the place where you can buy it from.

Gurgaon-based start-up Staqu Technologies Pvt. Ltd is redefining image search through AI. Launched in 2015 by Co-founders Anurag Saini, Chaitan Rexwal, Pankaj Sharma and Atul Rai, the start-up utilizes state-of-the-art deep learning technologies to provide precise, reverse image search solutions.

The search engine is designed to retrieve exact matches from the indicated database for an input image. Various algorithms like content, color, shape and texture are extracted and fused together to provide visual search solutions for various e-commerce businesses in the field of fashion, housing, medicine etc.

In a chat with Entrepreneur India, Atul Rai, also the CEO of Staqu explained how AI is making fashion images searches simpler.

Over 70 per cent of the content of e-commerce sites is images. Very few companies like Google, Microsoft and Facebook are using AI in images. Every company, be it an original equipment manufacturer (mobile companies) or e-commerce, is generating certain kind of data. We are trying to extract information from that image data and decode it for different purposes, Rai said.

AI Image Search

Rai said most people dont find a product similar to what they have seen on TV or found someone wearing it, in the market. Using this technology, one can actually spot the same outfit online at a different price.

Lets take the example of the e-commerce space. Suppose you want to buy an attire which an actress owns, its difficult for you to get the same as human brain cant describe the design in words. This is where the role of image data comes into play. You can take a picture of the dress you want and find out a similar dress based on the pixel information instead of text information, he stressed.

Rais start-up is selling the technology to e-commerce companies and mobile phone brands to integrate the same within the mobile phone camera.

We have joined hands with a company that has launched cell phones called Karbonn Fashion Eye and Aura Note2 which use our image search feature. We are also working with smartphone brands like Intex and Panasonic, he added.

How Staqu Knows a Particular Brand

Elaborating the role of AI in Staqu , Rai stressed that image search was not the only segment in which they are working right now. The start-up is offering various services like visual search, automated meta-tag generation, visual recommendation, real time video processing etc.

If you go to Flipkart and look for any dress of a particular brand, the brand name is a text which has to be there. For that, a lot of companies are putting human curators, and it is turning out to be very costly affair. As for every product you have to put some tags for the ease of searching them. This is where AI services are required, where you just have to upload the image and start generating relevant tags for that particular product," he said.

Rai concluded by sharing a useful advice to aspiring entrepreneurs, who are looking to lead the race in the artificial intelligence segment. To start something in AI one should have skill and experience because you need to have the knowledge of understanding the type of technology that can solve a specific issue," he said.

A self confessed Bollywood Lover, Travel junkie and Food Evangelist.I like travelling and I believe it is very important to take ones mind off the daily monotony .

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This AI Start-up Will help You Ape Celeb Fashion Trends - Entrepreneur

Auditing is about analyzing, being able to collect information around the audited subject and understanding its connections to other relevant subjects or areas. Going forward, auditors will not only uncover issues and errors, but will also provide solutions. This means that the reports from internal audits will not only list errors and process flaws, but also potential solutions to issues in collaboration with the experts from the audited area...

In most cases, the issues addressed by the auditors are known to the audited area, but in the day-to-day context of business activities, these issues are mostly not considered as urgent. In the future audit needs to adopt its approach to generate a benefit for the audited area too. The most important task of an internal auditor is to be able to analyze the collected information, while the question part of an audit can be done by a junior auditor. By repeatedly asking why?, an auditor can collect large amounts of information which helps to understand the entire landscape around a subject. It enables the auditor to evaluate the facts and make assessments.

When I was working as an internal auditor, I was involved in a project that was searching for an early warning system using available technology. We were tasked to ask simple questions and to evaluate the collected facts about a particular subject. In contrast to Eliza which became famous decades ago for being a revolutionary IT solution, the operating system featured in the movie HER did not provide a revolutionary new insight into the latest AI (Artificial Intelligence) technology.

Humans can differentiate between a conversation with a human and a conversation with a robot. Furthermore, they are able to make jokes, write poetry or recognise a lie by a persons voice or mimic. Can AI such as Eliza take over the activity of internal auditing? As mentioned, by asking the simplest questions, almost everybody can access information. How intelligent must AI become in order to be able to act as an internal auditing system, and what would be the role of humans in this process? I think that by even having simple artificial interaction software in place, the interview part, as well as the structuring of the collected information, can be taken over by a computer. The collected information can help clarify subjects and to draw conclusions about the problem.

Here is an example of how a simple question can be asked to collect all needed information about a particular issue: The issue is that I get up early in the morning.

AI: Why? Answer: Because I like the early morning energy and silence.

AI: Why do you like silence? Answer: Because, if its quiet, I get into a different state of mind with little effort.

AI: Why do you need a different state of mind? Answer: Because in a different state of mind I can see ordinary things from a new perspective.

AI: Why do you need a new perspective? Answer: Because different perspectives can reveal new solutions to problems.

AI: Why? Answer: Because new solutions will give me the ability to better solve outstanding issues.

AI: Why? Answer: Because by better solving my issues I have a better day .

In practice, questions can be chosen in a way that the person being interviewed does not discover that the questions are asked without the answers being listened to.

After the financial crisis, the area that would benefit the most from the introduction of AI into its processes is internal audit. This way, existing resources can be used in a more efficient way and it would be possible to audit more areas in shorter period of time. In the future, internal audit will use software like Eliza to interview experts from all areas almost monthly and will be able to collect information by setting up an early warning system for reporting by searching for critical words.

Areas with the most critical words will be audited with the most urgency. The interview can also include hidden checks to ensure that the person understands the answers being given and validates the truth. This will help to control more efficiently, without any additional resources. Furthermore, issues can be found more quickly, potential losses can be detected much earlier and before they cause damage to the organization...

Source: Banks of the Future, by Ella Thuiner; Published by Springer, 2015

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Artificial Intelligence and Internal Audit - HuffPost

The fourth industrial revolution

As artificial intelligence is deployed in the realm of customer service, telecom companies are showing increased interest in a number of these tools. Like previous industrial revolutions, many worry whether these technological innovations are weeding out human jobs. What many do not consider is the kinds of jobs A.I. can create.

But what exactly is A.I.? To begin with, its more than automation. Automation refers to computers or programs capable of performing repetitive, human tasks, but that doesnt mean automation itself is intelligent. By contrast, A.I. is an effort to enable computers to perform tasks that demand the ability to reason, solve problems, perceive and understand language.

There are three key positions advancements in A.I. could open: trainers, explainers and sustainers. Trainers teach A.I. algorithms how to mirror human behavior, and keep language processing and translating errors down to a minimum. Explainers serve as the middlemen between technologies and industry leaders, communicating the intricacies of A.I. algorithms to nontechnical staff. And managers uphold A.I. systems to legal and ethical norms.

As the maturity of A.I. moves out of academia, which its still kind of on the edge of, and to commercially hardened software and capability, I think youll see some these data science roles that you hear everybody hiring morph into their ability to adapt the products that are in the market to their specialty needs, explained JC Ramey, CEO of DeviceBits. And so that will create higher tech jobs, and most of those should be domestic based on where we see a lot of the hiring for the data science groups that we work with.

In terms of higher-tech jobs, chatbots, for instance, are answering basic tier-one calls at off-shore call centers instead of live agents. Technical questions are forwarded to tier 2 where the customer can talk to a person. This may eliminate several off-shore jobs for tier 1 calls, but it could provide companies with the means to invest in more tier-2 jobs. Ramey said he believes many of these jobs could be based in the U.S.

Technocrats have long pointed how automation can help workers take on more fulfilling tasks. But A.I. extends beyond automation. According to a survey of 352 A.I. researchers, there is a 50% chance A.I. will outperform all human tasks in 45 years, and that all human jobs will be automated in 120 years. The real question isnt whether A.I. can create jobs, but whether it can outmatch the numbers of jobs it takes.

I think this retooling will scare a lot of people and that there are some people who will not be able to make the shift, said Ramey, but the machinery and ecosystem that its creating at the same time creates a completely different market of jobs than whats available today.

The fruits of A.I. are discussed more than its limitations. Facebook, for instance, had to put efforts to build a chatbot for Messenger on hold after its bots hit a 70% failure rate. No budding technology is without glitches. However, the acceptable failure rate for these projects has yet to be clearly defined, which can help inform whether a technology is worth a long-term investment.

I think knowledge engineering is the biggest level of limitation, said Ramey. Today, people think it is the silver bullet. I think everyone who is thinking a bot is an A.I., but the reality is the knowledge engineering that has to happen underneath to give that bot a starting point, and how do you train that bot overtime, is still the big gap, and that is the limitation that we see as a big opportunity in the market-to-sell.

Risks versus benefits aside, several tech giants like Apple, Facebook, Google and IBM believe A.I. has a future worth investing in. The telecom ecosystem will likely absorb A.I. tools as it becomes more complex. I think we will look back in ten years and realize A.I. created a whole new sector for us and gave us another bump like the dot com boom did, said Ramey.

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Can artificial intelligence help create jobs? - RCR Wireless News

Google has become very interested in artificial intelligence in recent years, and particularly its applications for regular people. For example, here's a load of experiments that it's running involving machine learning.

Now, however, researchers at the Texas Advanced Computing Center have shown how artificial intelligence techniques can also deliver better search engine results. They've combined AI, crowdsourcing and supercomputers to develop a better system for information extraction and classification.

At the 2017 Annual Meeting for the Association of Computational Linguistics in Vancouver this week, associate professor Matthew Lease led a team presenting two papers that described a new kind of informational retrieval system.

"An important challenge in natural language processing is accurately finding important information contained in free-text, which lets us extract it into databases and combine it with other data in order to make more intelligent decisions and new discoveries," Lease said.

"We've been using crowdsourcing to annotate medical and news articles at scale so that our intelligent systems will be able to more accurately find the key information contained in each article."

They were able to use that crowdsourced data to train a neural network to predict the names of things, and extract useful information from texts that aren't annotated at all.

In the second paper, they showed how to weight different linguistic resources so that the automatic text classification is better. "Neural network models have tons of parameters and need lots of data to fit them," said Lease.

In testing on both biomedical searches and movie reviews, the system delivered consistently better results than methods that didn't involve weighting the data.

"We had this idea that if you could somehow reason about some words being related to other words a priori, then instead of having to have a parameter for each one of those word separately, you could tie together the parameters across multiple words and in that way need less data to learn the model," said Lease.

He added: "Industry is great at looking at near-term things, but they don't have the same freedom as academic researchers to pursue research ideas that are higher risk but could be more transformative in the long-term."

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How artificial intelligence can help deliver better search results - TechRadar

In this sponsored post,Ramnath Sai Sagar, Marketing Manager at Mellanox Technologies, explores how recent advancements in Artificial Intelligence, especially deep learning, are set to make an impact in the field ofastronomy and astrophysics.

Ramnath Sai Sagar, Marketing Manager at Mellanox Technologies

Since the dawn of the space age, unmanned spacecraft have flown blind, with little to no ability to make autonomous decisions based on their environment. That, however, changed in the early 2000s, when NASA started working on leveraging Artificial Intelligence (AI) and laying the foundation that would help Astronauts and Astronomers to work more efficiency in Space. In fact, just last month, NASAs Jet Propulsion Laboratory published how AI will govern the behavior of space probes.

Recent advancements in Artificial Intelligence, especially Deep Learning (a subfield in AI), are set to make a deeper impact in the field of astronomy and astrophysics. From navigating the unknown terrain of Mars, to analyzing petabytes of data generated from Square Kilometer Array, to finding Earth-like planets in our messy galaxy, AI is already revolutionizing our lives here on earth by building smarter and more autonomous cars, helping us find solutions to climate change, revolutionizing healthcare and much more. Mellanox is proud to be working closely with the leading companies and research organizations to make advancements in the field of Artificial Intelligence and Astronomy.

AI: The Next Industrial Revolution

Coined in 1956 by Dartmouth Assistant Professor John McCarthy, AI existed before the Race to Space but could only deliver rudimentary displays of intelligence in specific context. Progress was limited due to the complexities of algorithms needed to tackle various real-world issues. Many were above the ability of a mere human to execute. This however, changed in the past decade mainly due to two reasons:

Due to this, AI now presents one of the most exciting and potentially transformative opportunities for the mankind. In fact, in some quarters it is being heralded as the next industrial revolution:

The last 10 years have been about building a world that is mobile-first. In the next 10 years, we will shift to a world that is AI-first. Sundar Pichai, CEO of Google, October 2016

AI for the Messy Galaxy

While humanity has made great strides in exploring the observable universe, we need to rely on intelligent robots to explore where we cannot humanly go. This is because our galaxy, the Milky Way, is one messy place, filled with cosmic dust from stars, comets, and more; concealing the very things scientists want to study. That said, there are three major challenges in leveraging AI in the future of space exploration. Firstly, the probes will have to be able to learn about and adapt to unknown environments including responding to thick layers of gas in a planets atmosphere, extreme temperatures or unplanned for fluctuations in gravity.

Secondly, when a probe falls outside the communication range, would have tofigure out when and how to return the data collected during the time the signal was lost. Finally, given the vast distances in space, it could take several generations before the probe reaches its destination and therefore, will need to be flexible enough to adapt to any new discoveries and innovations we make here on earth. The solution to these problems will require training AI models on petabytes of data captured using supercomputers.

The benefits of using AI to control space-exploring robots are already being realized by missions that are currently underway. For example, Opportunity, the Mars Exploration Rover, which was launched back in 2003, has an AI driving system called Autonav that allows it to explore the surface of Mars. In addition, Autonomous Exploration for Gathering Increased Science (AEGIS) has been used by the NASA Mars rover, Curiosity, since May in order to select which aspects of Mars are particularly interesting and subsequently take photos of.

Image Captured by AEGIS Enabled Curiositys ChemCam.

But Mars is by no means the final destination and the exploration of more challenging destinations will require even more advanced AI. For example, exploring the subsurface ocean of the Jovian moon Europa in the hope of finding alien life, will require bypassing a thick (~10km) ice crust. Controlling this exploration would be severely limited without advanced autonomy.

Artificial Intelligence Needs Intelligent Network

Since the early age of Mellanox, we have been working closely with NASA and many research labs help solve the challenges of scientific computing, whether its the aerodynamic simulation of the Jet Propulsion Engine or monitoring the universe in unprecedented detail. In addition, over the last few years, Mellanox has also enabled the pioneers in the field of AI including Baidu for their advancements in autonomous cars and Yahoo for image recognition. The applications of autonomous driving and object recognition go far beyond the limits of Earth and Mellanox is proud to be working closely with several research organizations and companies and helping them achieve technological breakthroughs in the field of astronomy and astrophysics.

Forty-eight years ago, Neil Armstrong said Thats one small step for man, one giant leap for mankind, when he became the first human to set the foot on the surface of the moon. The next giant leap for mankind will come from the small step of a robot, powered by AI and Mellanox.

Ramnath Sai Sagar is Marketing Manager at Mellanox Technologies. This post originally ran as part of Mellanox Technologies Interconnected Planet blog series.

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Artificial Intelligence: A Journey to Deep Space - insideHPC