live from last vegas: during CES 2020, LG unveiled their framework for the future of artificial intelligence (AI) development. throughout its both massive and impressive booth, the south korean multinational electronics company showcased the LG ThinQ technology one that goes a step further by using deep learning to predict and play a proactive role in the users life. from a fridge that can tell you when to buy milk, to a dressing room that picks the best outfits for your body type, LG is giving us a glimpse into the future.

live from las vegas:designboom is covering all the latest launches ofCES 2020 the worlds largest consumer technology fair from the trade show grounds. stay connected for the future of technology as it debuts.

images courtesy of LG unless otherwise stated

at CES 2020, the LG ThinQ features were on show, displaying everyday products for attendants to try out for themselves. each one of them followed the four levels of AI experience (AIX) stated by the company efficiency, personalization, reasoning and exploration. efficiency is where specific device and system functions can be automated through single commands, like voice recognition. personalization focuses on pattern learning to optimize and personalize device functions. reasoning envisions an AI that can perceive the cause of certain patterns and behaviors to predict and promote positive outcomes for users. and although still far in the future, level four, exploration, is the ultimate destination for LGs AI. by using a concept called experimental learning based on the scientific method, AI-enabled systems will be able to develop new capabilities through forming and testing hypotheses to uncover new inferences, enabling them to learn and improve, adding more value to users lives.

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LGs massive exhibition space at CES 2020 embodied the companys anywhere is home concept with the LG ThinQ zone, showcasing a truly connected lifestyle that extends beyond the front door. the experience began with the smart door, which verifies visitors with both facial recognition and vein authentication before unlocking. when exiting, users can see a screen on the inside of the door that displays useful information such as weather and traffic conditions. when set to depart mode, the smart door instructs LG ThinQ appliances to go into low power when all residents have left the house.

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when it comes to fashion, the LG ThinQ fit collection zone allowed visitors to experience virtual fashion without having to step into a fitting room. an evolution of LGs original smart mirror concept, the LG ThinQ fit system uses cameras to accurately measure the users body to generate a realistic avatar for virtual fittings.

LGs robotic solutions impressed attendees with their culinary skills, efficiency and first-class hospitality at CLOis table zone. the futuristic restaurant featured the LG CLOi robots managing the entire operation from taking orders, cooking, serving and cleaning. potential diners would make reservations remotely via the ThinQ app and browse the menu via a smart speaker, smart TV or smartphone.

last but not least, the connected car zone, LG demonstrated a personalized in-car experience that allows users to take a piece of home on the road. for example, the vehicle features OLED displays inside it where users can continue enjoying the TV programs and movies they started watching at home. moreover, the personal sound zone offers a unique multimedia experience for the rider with voice-activated virtual personal assistant.

as pioneers in the field of AI it is our responsibility to consider the importance of the human experience whilst pushing the boundaries of AI research and development, commented jean-franois gagn, co-founder and CEO of element AI. together with LG electronics we hope that this work helps to set forth standards and principles that guide AI practitioners to consider a human centric approach when building the future.

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project info:

name: LG ThinkQ

company: LG

presented at: CES 2020

juliana neira I designboom

jan 13, 2020

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LG's ThinQ technology showcases the virtue of artificial intelligence in fashion and food at CES 2020 - Designboom

Image credit: Depositphotos

Welcome to TechTalksAI book reviews, a series of posts that explore the latest literature on AI.

It wouldnt be an overstatement to say that artificial intelligence is one of the most confusing and least understood fields of science. On the one hand, we have headlines that warn of deep learning outperforming medical experts, creating their own language and spinning fake news stories. On the other hand, AI experts point out that artificial neural networks, the key innovation of current AI techniques, fail at some of the most basic tasks that any human child can perform.

Artificial intelligence is also marked with some of the most divisive disputes and rivalries. Scientists and researchers are constantly quarreling over the virtues and shortcomings of different AI techniques, further adding to the confusion and chaos.

Between tales of killer drones and robots that cant brew a cup of coffee, a book that sheds light on the real capabilities and limits of artificial intelligence is an invaluable and necessary read. And this is exactly how I would describe Artificial Intelligence: A Guide for Thinking Humans, a book by computer science professor Melanie Mitchell.

With a rich background in artificial intelligence and computer science, Mitchell sorts out, in her own words, how far artificial intelligence has come, and elucidates AIs disparateand sometimes conflictinggoals. As AI is drawing growing attention from investors, governments and the media, Mitchells Guide for Thinking Humans lays out the good, bad and ugly of artificial intelligence.

Most of us reading news headlines view artificial intelligence in the context sensational articles that have started to appear in mainstream media in the past few years. But theres much more to AI, which has a history that dates back to the early days of computing.

A Guide for Thinking Humans demystifies some of the least understood facts about artificial intelligence. As you read through the chapters, Mitchell eloquently takes you through the six-decade history of AI. You become acquainted with the original vision of AI, the early efforts at creating symbolic AI and expert systems, and parallel efforts to develop artificial neural networks.

You go through the AI winters, where overpromising and underdelivering dampened interest and funding in artificial intelligence. One of the most important parts of the book is the chapter convolutional neural networks (CNN), the AI technique that triggered the deep learning revolution in the early 2010s. While digging into the inner workings of CNNs, Mitchell also explains how other scientific fields such as neuroscience and cognitive science have played a crucial role in advancing AI.

Today convolutional neural networks and deep learning, in general, are a pertinent component of many applications we use every day.

It turns out that the recent success of deep learning is due less to new breakthroughs in AI than to the availability of huge amounts of data (thank you, internet!) and very fast parallel computer hardware, Mitchell notes in A Guide for Thinking Humans. These factors, along with improvements in training methods, allow hundred-plus-layer networks to be trained on millions of images in just a few days.

The trend, sparked by the ImageNet competition, has gradually morphed the field from an academic contest to a high-profile sparring match for tech companies commercializing computer vision, Mitchell explains.

The commercialization of AI has had bad effects on the field (as Ive also argued in these pages). Mitchell points to some of the other negative effects of the race to beat tests and benchmarks. Precision at ImageNet has become a de facto ticket to getting funding and improving stock prices and product sales. It has also led some companies and organizations to cheat their way to better test results without proving robustness in real-world situations.

These systems can make unpredictable errors that are not easy for humans to understand, Mitchell said in written comments to TechTalks. The machines often are not able to deal with input that is different from the kind of input they have been trained on. A Guide for Thinking Humans provides several examples of AIs failures.

There are several studies that show deep learning models optimized for ImageNet do not necessarily perform well when faced with objects in real life. There are also numerous papers that show how neural networks can make dangerous mistakes.

Mitchell also points out that, while very efficient at processing vast amounts of data, current AI models lack the generalization abilities of human intelligence, which makes them vulnerable to the long-tail problem: the vast range of possible unexpected situations an AI system could be faced with. Unfortunately, current approaches to AI only try to solve these problems by throwing more data and compute at the problem.

Often these benchmark datasets dont force the learning systems to solve the actual full problem that humans want them to solve (e.g., object recognition) but allow the learning systems to use shortcuts (e.g., distinguishing textures) that work well on the benchmark dataset, but dont generalize as well as they should, Mitchell says.

The obsession with creating bigger datasets and bigger neural networks has sidelined some of the important questions and areas of research regarding AI. Some of these topics include causality, reasoning, commonsense, learning from few examples and other fundamental elements that todays AI technology lacks.

But at least, the ImageNet race has taught us one thing. Says Mitchell: It seems that visual intelligence isnt easily separable from the rest of intelligence, especially general knowledge, abstraction, and language Additionally, it could be that the knowledge needed for humanlike visual intelligence cant be learned from millions of pictures downloaded from the web, but has to be experienced in some way in the real world.

Fortunately, these are topics that have been gaining increasing attention in the past year. In his 2019 NeurIPS keynote speech, deep learning pioneer Yoshua Bengio discussed system 2 deep learning, which aims to solve some of these fundamental problems. While not everyone agrees with Bengios approach (and its not clear which approach will work), the fact that these things are being discussed is a positive development.

One of the least-understood aspects of artificial intelligence is its handling of human language. The advances in the field have been tremendous. Machine translation has taken leaps and bound thanks to deep learning. Search engines are producing much more meaningful results. There are AI algorithms that can pass science tests. And of course, theres that OpenAI text generation algorithm that threatens to create a massive fake new crisis.

There has also been remarkable progress in speech recognition, an area where neural networks perform especially well (Mitchell calls it AIs most significant success to date in any domain). It is thanks to deep learning that you can utter commands to Alexa, Siri, and Cortana. Your Gmails Smart Compose and sentence completion features are powered by AI. And the numerous chatbot applications that have found a stable user base all leverage advances in natural language processing (NLP).

As Mitchell told TechTalks, I think some of these advances are very positive developments; applications such as automated translation, speech recognition, etc. certainly make life better. Indeed, human-machine combination is much better today than at any time in the past.

But whats less understood is how much todays artificial intelligence systems understand the meaning of language.

Understanding languageincluding the parts that are left unsaidis a fundamental part of human intelligence, Mitchell explains in A Guide for Thinking Humans. Language relies on commonsense knowledge and understanding of the world, two areas where todays AI lacks sorely.

Todays machines lack the detailed, interrelated concepts and commonsense knowledge that even a four-year-old child brings to understanding language, Mitchell writes.

And its true. Even the most sophisticated language models start to break as soon as you test their limits. For the moment, AI is limited to handling small amounts of text. Alexa can perform thousands of tasks, but it cant hold a meaningful conversation. Smart Compose provides interesting reply suggestions, but theyre only short answers to basic queries. Google Translate produces decent results when you want to translate simple sentences. But it cant translate an article that contains the rich and complicated nuances of language and culture. And the text generated by OpenAIs famous GPT-2 language model loses coherence as it becomes longer.

This is because todays AI still lacks the understanding of language. So how is AI performing such feats? It is basically the same pattern-matching that neural networks are performing on images (though in a different manner and with some added tricks). Again, recent years have proven that bigger data sets and larger neural networks will help push the limits of NLP applications. But they wont result in breakthroughs.

Whats stunning to me is that speech-recognition systems are accomplishing all this without any understanding of the meaning of the speech they are transcribing Many people in AI, myself included, had previously believed that AI speech recognition would never reach such a high level of performance without actually understanding language. But weve been proven wrong, Mitchell writes in A Guide for Thinking Humans.

But as she later explains in the book (and the failures of AI show), theres only so much you can achieve with statistics and pattern matching. For the moment, AI systems might have solved 90 percent of the problem of solving language problems. But that last 10 percent, dealing with the implicit subtleties and hidden meanings of language, remain unsolved.

Whats needed to power through that last stubborn 10 percent? More data? More network layers? Or, dare I ask, will that last 10 percent require an actual understanding of what the speaker is saying? Mitchell reflects. Im leaning toward this last one, but Ive been wrong before.

So while we enjoy the applications of artificial intelligence in natural language processing, theres no reason to worry that robots will soon replace human writers or interpreters. While neural machine translation can be impressively effective and useful in many applications, the translations, without post-editing by knowledgeable humans, are still fundamentally unreliable, Mitchell observes.

A Guide for Thinking Humans delves into many more topics, including the ethics of AI, the intricacies of the human mind, and the meaning of intelligence. Like several other scientists, Mitchell notes in her book that in intelligence, the central notion of AI, remains ill-defined, with various meanings, an over-packed suitcase, zipper on the verge of breaking.

The attempt to create artificial intelligence has, at the very least, helped elucidate how complex and subtle are our own minds, Mitchell writes.

See the article here:
Artificial intelligence: The good, the bad and the ugly - TechTalks

This is the second part in aseries looking at the investments for the future sectors that will grow to become major industries and provide returns along the way. Part onelooks at clean energy. We will also focus on, water security, ageing populations and nutrition

"Alexa, can I make money investing in companiesthat buildartificial intelligence (AI) programmes?"

There is a lot of hype around the technology andit has thepotential to transform our lives. This naturally has led to investors approaching the sector with interest, looking to see whether they can invest in the next big technological change.

Investing in something as specific as AI is known as thematic investing or trend investing. Thisis a way of getting exposure to one niche area that is expected to expand significantly over time and therefore grow an investment.

Investing in AI is the second part in aseries looking at trend investing. Telegraph Money studies the outlook AI companies, how they would withstand a recession and what is the best way to invest for those enamoured with the sector.

AI is beginning to touch all areas of our lives, from suggesting films on Netflix to helping doctors diagnose diseases. It even interacts with us in our homes via smart speakers, which can play music and answer questionsamong an increasingly sophisticated array of "skills".

While AI dates back to the work of computer pioneer Alan Turing in the 1940s, its usefulness for the masses could be at a turning point. Research from Microsoft showed that around half of British businesses are now using AI in some form.

See the original post here:
Artificial intelligence is changing the world here's how to invest - Telegraph.co.uk

Over the years, the influence of artificial intelligence (AI) has spread to almost every aspect of the travel and the hospitality industry. Thirty percent of hospitality businesses use AI to augment at least one of their primary sales processes, and most customer personalisation is done using AI.

The proliferation of AI in the travel and hospitality industry can be credited to the humongous amount of data being generated today. AI helps analyse data from obvious sources, brings value in assimilating patterns in image, voice, video, and text, and turns it into meaningful and actionable insights for decision making. Trends, outliers, and patterns are figured out using machine learning-based algorithms that help in guiding a travel or hospitality company to make informed decisions.

Lets take a close look at the AI-driven application areas in the travel and hospitality industry and the impact on the ensuing business value chain:

There are always a few trailblazers who are up for a new challenge and adopt new-age exponential technologies. Many hotel chains have started using an AI concierge. One great example of an AI concierge is Hilton World wides Connie, the first true AI-powered concierge bot. Connie stands at two feet high and guests can interact with it during their check-in. Connie is powered by IBMs Watson AI and uses the Way Blazer travel database. It can provide succinct information to guests on local attractions, places to visit, etc. Being AI-driven with self-learning ability, it can learn and adapt and respond to each guest on personalised basis.

In the travel business, Mezi, using AI with Natural Language Processing technique, provides a personalised experience to business travellers, who usually are strapped for time. It talks about bringing on a concept of bleisure(business+leisure) to address the needs of the workforce. The companys research shows that 84 percent of business travellers return feeling frustrated, burnt out, and unmotivated. The kind of tedious and monotonous planning that goes into the travel booking could be the reason for it. With AI and NLP, Mezi collects individual preferences and generates personalised suggestions so that a bespoke and streamlined experience is given and the issues faced are addressed properly.

Increased productivity now begins with the search for the hotel, and sophisticated AI usage has paved the way for the customer to access more data than ever before. Booking sites like Lola.com provides on-demand travel services and have developed algorithms that can not only instantly connect people to their team of travel agents who find and book flights, hotels, and cars, but have been able to empower their agents with tremendous technology to make research and decisions an easy process.

Chatbot technology is another big strand of AI, and not surprisingly, many travel brands have already launched their own versions in the past year or so. Skyscanner is just one example, creating an intelligent bot to help consumers find flights in Facebook Messenger. Users can also use it to request travel recommendations and random suggestions. Unlike ecommerce or retail brands using chatbots, which can appear gimmicky, there is an argument that examples like Skyscanner are much more relevant and useful for everyday consumers. After all, with the arrival of many more travel search websites, consumers are being overwhelmed by choice not necessarily helped by it.Consequently, a chatbot like Skyscanner is able to cut through the noise, connecting with consumers in their own time and in the social media spaces they most frequently visit.

Recently, Aero Mexico started using Facebook Messenger chatbot to answer very generic customer questions. The main idea was to cater to 80 percent of questions, which are usually repeat ones and about common topics. Thus, AI is of great application to avoid a repetitive process. Airlines hugely benefit from this. KLM Royal Dutch Airlines uses AI to respond to the queries of customers on Twitter and Facebook. It uses an algorithm from a company called Digital Genius, which is trained on 60,000 questions and answers. Not only this, Deutsche Lufthansas bot Mildred can help in searching the cheapest fares.

International hotel search engine Trivago acquired Hamburg, Germany machine learning startup Tripl as it ramps up its product with recommendation and personalisation technology, giving them a customer-centric approach. The AI algorithm gives tailored travel recommendations by identifying trends in users social media activities and comparing it with in-app data of like-minded users. With its launch, users could sign up only through Facebook, potentially sharing oodles of profile information such as friends, relationship status, hometown, and birthdays.

Persona-based travel recommendations, use of customised pictures and text are now gaining ground to entice travel. KePSLAs travel recommendation platform is one of the first in the world to do this by using deep learning and NLP solutions. With 81 percent of people believing that intelligent machines would be better at handling data than humans, there is also a certain level of confidence in this area from consumers.

Dorchester Collection is another hotel chain to make use of AI. However, instead of using it to provide a front-of-house service, it has adopted it to interpret and analyse customer behaviour deeply in the form of raw data. Partnering with technology company, Richey TX, Dorchester Collection has helped to develop an AI platform called Metis.

Delving into swathes of customer feedback such as surveys and reviews (which would take an inordinate amount of time to manually find and analyse), it is able to measure performance and instantly discover what really matters to guests. Mtis helped Dorchester to discover that breakfast it not merely an expectation but something guests place huge importance on. As a result, the hotels began to think about how they could enhance and personalise the breakfast experience.

Flight fares and hotel tariffs are dynamic and vary on real-time basis, depending on the provider. No one has time to track all those changes manually. Thus, intelligent algorithms that monitor and send out timely alerts with hot deals are currently in high demand in the travel industry.

Trivago and Make my trip are screening through swamp of data points, variables, and demand and supply patterns to recommend optimised travel and hotel prices. The AltexSoft data science team has built such an innovative fare predictor tool for one of their clients, a global online travel agency, Fareboom.com. Working on its core product, a digital travel booking website, they could access and collect historical data about millions of fare searches going back several years. Armed with such information, they created a self-learning algorithm, capable of predicting future price movements based on a number of factors, such as seasonal trends, demand growth, airlines special offers, and deals.

While the previous case is focused mostly on planning trips and helping users navigate most common issues while traveling, automated disruption management is somewhat different. It aims at resolving actual problems a traveller might face on his/her way to a destination point. Mostly applied to business and corporate travel, disruption management is always a time-sensitive task, requiring instant response.

While the chances of getting impacted by a storm or a volcano eruption are very small, the risk of a travel disruption is still quite high: there are thousands of delays and several hundreds of cancelled flights every day. With the recent advances in AI, it became possible to predict such disruptions and efficiently mitigate the loss for both the traveller and the carrier. The 4site tool, built by Cornerstone Information Systems, aims to enhance the efficiency of enterprise travel.

The product caters to travellers, travel management companies, and enterprise clients, providing a unique set of features for real-time travel disruption management. In an instance, if there is a heavy snowfall at your destination point and all flights are redirected to another airport, a smart assistant can check for available hotels there or book a transfer from your actual place of arrival to your initial destination.

Not only are passengers are affected by travel disruptions; airlines bear significant losses every time a flight is cancelled or delayed. Thus, Amadeus, one of the leading global distribution systems (GDS), has introduced a Schedule Recovery system, aiming to help airlines mitigate the risks of travel disruption. The tool helps airlines instantly address and efficiently handle any threats and disruptions in their operations.

Future potential: So, reflecting on the above-mentioned use cases of the travel and hospitality industry leveraging Ai to a large extent, there will be few latent potential areas in the industry that will embrace AI in the future :

Due to the greater need for structure and less of a desire for discovery, it certainly makes sense that AI would be more suited to business travellers. Specifically, it could help to simplify the booking process for companies, and help eliminate discrepancies around employee expenses. With reducing costs and improving efficiency two of the biggest benefits, AI could start to infiltrate business travel even more so than leisure in the next 12 months.

Lastly, we can expect to see greater development in conversational AI in the industry. With voice-activated search, the experience of researching and booking travel has the potential to become quicker and easier than ever before. Similarly, as Amazon Echo and Google Home start to become commonplace, more hotels could start to experiment with speech recognition to ramp up customer service. This means devices and botscould become the norm for brands in the travel and hospitality industry.

The travel and hospitality industry transformation will morph into experience-driven and asset-light business, and wide adoption of AI will usher a new-age customer experience and set a benchmark for other industries to emulate. Fasten your seat belts AI will redefine the travel and hospitality industry.

(Edited by Teja Lele Desai)

(Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the views of YourStory.)

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Reimagining the future of travel and hospitality with artificial intelligence - YourStory

Climate-driven change in the Gulf of Maine is raising new threats that red tides will become more frequent and prolonged. But at the same time, powerful new data collection techniques and artificial intelligence are providing more precise ways to predict where and when toxic algae will bloom. One of those new machine learning prediction models has been developed by a former intern at Bigelow Labs in East Boothbay.

In a busy shed on a Portland wharf, workers for Bangs Island Mussels sort and clean shellfish hauled from Casco Bay that morning. Wholesaler George Parr has come to pay a visit.

I wholesale to restaurants around town, and if theres a lot of mackerel or scallops, Ill ship into Massachusetts, he said.

But business grinds to a halt, he said, when blooms of toxic algae suddenly emerge in the bay causing the dreaded red tide.

Toxins can build in filter feeders to levels that would cause Paralytic Shellfish Poisoning in human consumers. State regulators shut down shellfish harvests long before danger grows acute. But when a red tide swept into Casco Bay last summer, Bangs Islands harvest was shut down for a full 11 weeks.

So when the restaurants cant get Bangs Island theyre like Why cant we get Bangs Island? It was really bad this summer. And nobody was happy.

As Parr notes, businesses of any kind hate unpredictability. And being able to forecast the onset or departure of a red tide has been a challenge although thats changing with the help of a type of artificial intelligence called machine learning.

Were coming up with forecasts on a weekly basis for each site. For me thats really exciting. Thats what machine learning is bringing to the table, said Izzi Grasso, a recent Southern Maine Community College student who is now seeking a mathematics degree at Clarkson University.

Last summer Grasso interned at the Bigelow Laboratory for Ocean Sciences in East Boothbay. Thats where she helped to lead a successful project to use cutting-edge neural network technology that is modeled on the human brain to better predict toxic algal blooms in the Gulf of Maine.

Really high accuracy. Right around 95 percent or higher, depending on the way you split it up, she said.

Heres how the project worked: the researchers accessed a massive amount of data on toxic algal blooms from the state Department of Marine Resources. The data sets detailed the emergence and retreat of varied toxins in shellfish samples from up and down the coast over a three-year period.

The researchers trained the neural network to learn from those thousands of data points. Then it created its own algorithms to describe the complex phenomena that can lead up to a red tide.

Then we tested how it would actually predict on unknown data, said Grasso.

Grasso says they fed in data from early 2017 which the network had never seen and asked it to forecast when and where the toxins would emerge.

I wasnt surprised that it worked, but I was surprised how well it worked, the level of accuracy and the resolution on specific sites and specific weeks, said Nick Record, Bigelows big data specialist.

Record says that the networks accuracy, particularly in the week before a bloom emerges, could be a game-changer for the shellfish industry and its regulators.

Once its ready, that is.

Basically it works so well that I need to break it as many ways as I can before I really trust it.

Still, the work has already been published in a peer-reviewed journal, and it is getting attention from the scientific community. Don Anderson is a senior scientist at the Woods Hole Oceanographic Institution who is working to expand the scope of data-gathering efforts in the Gulf.

The world is changing with respect to the threat of algal blooms in the Gulf of Maine, he said. We used to worry about only one toxic species and human poisoning syndrome. Now we have at least three.

Anderson notes, though, that machine-learning networks are only as good as the data that is fed into them. The Bigelow network, for instance, might not be able to account for singular oceanographic events that are short and sudden or that havent been captured in previous data-sets such as a surge of toxic cells that his instruments detected off Cutler last summer.

With an instrument moored in the water there, and we in fact got that information, called up the state of Maine and said youve got to be careful, theres a lot of cells moving down there, and they actually had a meeting, they implemented a provisional closure just on the basis of that information, which was ultimately confirmed with toxicity once they measured it, said Anderson.

Anderson said that novel modeling techniques such as Bigelows, coupled with an expanded number of high-tech monitoring stations, like Woods Hole is pioneering in the Gulf, could make forecasting toxic blooms as simple as checking the weather report.

That situational awareness is what everyones striving to produce in the field of monitoring and management of these toxic algal blooms, and its going to take a variety of tools, and this type of artificial intelligence is a valuable part of that arsenal. Back at the Portland wharf, shellfish dealer George Parr says the research sounds pretty promising.

Forewarned is fore-armed, Parr said. If they can figure out how to neutralize the red tide, thatd be even better.

Bigelow scientists and former intern Izzi Grasso are working now to look under the hood of the neural network, to figure out how, exactly, it arrives at its conclusions. They say that could provide clues about how not only to predict toxic algal blooms, but even how to prevent them.

See the article here:
Artificial Intelligence Could Help Scientists Predict Where And When Toxic Algae Will Bloom - Bangor Daily News

SEOUL (Reuters) - In cram school-obsessed South Korea, students fork out for classes in everything from K-pop auditions to real estate deals. Now, top Korean firms are rolling out artificial intelligence in hiring - and jobseekers want to learn how to beat the bots.

From his basement office in downtown Gangnam, careers consultant Park Seong-jung is among those in a growing business of offering lessons in handling recruitment screening by computers, not people. Video interviews using facial recognition technology to analyze character are key, according to Park.

Dont force a smile with your lips, he told students looking for work in a recent session, one of many he said he has conducted for hundreds of people. Smile with your eyes.

Classes in dealing with AI in hiring, now being used by major South Korean conglomerates like SK Innovation (096770.KS) and Hyundai Engineering & Construction (000720.KS), are still a tiny niche in the countrys multi-billion dollar cram school industry. But classes are growing fast, operators like Parks People & People consultancy claim, offering a three-hour package for up to 100,000 won ($86.26).

Theres good reason to see potential. As many as eight out of every 10 South Korean students are estimated to have used cram schools, and rampant youth unemployment in the country - nearly one in four young people are not in the workforce by certain measures, according to Statistics Korea - offers a motive not present in other countries where cram schools are popular, like Japan.

The AI wont be naturally asking personal questions, said Yoo Wan-jae, a 26-year-old looking for work in the hospitality industry. That will make it a bit uncomfortable ... Ill need to sign up for cram schools for the AI interview, said Yoo, speaking in Seouls Noryangjin district, known as Exam Village, packed with cram schools and study rooms.

Businesses around the world are experimenting with increasingly advanced AI techniques for whittling down applicant lists.

But Lee Soo-young, a director of Korea Advanced Institute of Science and Technology (KAIST) Institute for Artificial Intelligence, told Reuters by telephone the new technology is being more widely embraced in South Korea, where large employers wield much influence in a tightening job market.

According to Korea Economic Research Institute (KERI), nearly a quarter of the top 131 corporations in the country currently use or plan to use AI in hiring.

One AI video system reviewed by Reuters asks candidates to introduce themselves, during which it spots and counts facial expressions including fear and joy and analyses word choices. It then asks questions that can be tough: You are on a business trip with your boss and you spot him using the company (credit) card to buy himself a gift. What will you say?

AI hiring also uses gamification to gauge a candidates personality and adaptability by putting them through a sequence of tests.

Through gamification, employers can check 37 different capabilities of an applicant and how well the person fits into a position, said Chris Jung, a chief manager of software firm Midas IT in Pangyo, a tech hub dubbed South Koreas Silicon Valley.

Preparing for such tests doesnt necessarily involve simply memorizing answers. Some games dont even have a right answer, as they are aimed to spot the problem-solving attitude of the applicant, Jung said.

At People & People, consultant Park said he gave AI hiring talks to over 700 university students, graduates and lecturers in 2019.

Students are struggling from the emergence of AI interviews. My goal is to help them be fully prepared for what they will be dealing with, said Park.

In an online chat room monitored by Park, with more than 600 participants, numerous messages indicate thanks for the classes and success in AI interview quests.

But elsewhere, some who havent yet taken lessons have already given up.

Kim Seok-wu, a 22-year-old senior at a top university, recently failed to get beyond an AI interview for a management position at a retail company, and decided to pursue graduate school instead of trying to find a job.

I think I will feel hopeless if all companies go AI for hiring, Kim said. The AI interview is too new, so job applicants dont know what to prepare for and any preparations seem meaningless since the AI will read our faces if we make something up.

Reporting by Sangmi Cha; Editing by Jack Kim, Josh Smith and Kenneth Maxwell

Read the rest here:
'Smile with your eyes': How to beat South Korea's AI hiring bots and land a job - Reuters

The algorithm uses the frequency and severity of these patterns to judge the likelihood that a social media post or even news article has been falsified.

The researchers were able to verify their fake news checker algorithm against a database containing over 12,000 samples of Internet posts, which have already been pre-labeled by humans as either fake or real. The results showed that the software was accurate 99.4 percent of the time.

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But theres a problem. In Aldwairis line of work, a sample size of 12,000 is small. The researchers want to test their algorithm on more than 100 million data points, and its unrealistic that humans will ever be able to label that many posts as real or fake, for them to check their results against.

It requires a continuous learning process,says Jarrah, and in order to improve accuracy, you have to give it as many sources of news as possible to learn from.

To solve this quandary, theyre using a process known as machine learning in which computers, not humans, will label a social media post or news article as real or fake. This will end up with false positives, but thats where the earlier algorithm comes in.

Our algorithm should hopefully filter out these false positives, says Aldwairi.

Additionally, the corrected false positives will feed back into the machine learning to help it understand its mistakes and improve.

The end result of all of this would be a piece of software that gives a rating to the Internets vast network of websites on a sliding scale and notifies users of this score when they visit a given page.

Were talking about less than a year for it to be a downloadable product, says Jarrah.

While the machine learning is taking place, the researchers are hunting for suitable datasets in Arabic to teach their algorithm to work in both languages. We hope to finish that this year too, says Jarrah.

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Arab Researchers Use Artificial Intelligence in Bid to Thwart Fake News - Al-Fanar Media

Artificial Intelligence (AI) is probably one of the branches of Computer Science that is experiencing more growth nowadays. Even though it was born more than 70 years ago, it is in a historical period where it has generated more interest because of the revolution it has caused on the market today.

Until very recently, there were limited computational capacities that made Artificial Intelligence produce very poor results on the problem being applied, which resulted in several periods of historical dissatisfaction in the industry and considerable reduction in both interests. In this case, discipline on the number of dedicated researchers.

However, in recent years Artificial Intelligence has gained enormous momentum, able to solve problems with computers that were previously thought to be impossible, reaching levels that had never been reached before. Even mobile devices benefit from research in this area, for example, through keyboard predictive text, fingerprint screen unlocking, or face detection on a frame taken by a camera.

Several reasons that have functioned as AI takeoff machines can be listed, but the democratization of computational capacity stands out with particular emphasis, especially since 2009 with the publication of the first scientific article about the massive parallelization of AI calculations using GPUs and others in 2010, to demonstrate their use in automatic recognition of handwriting, exceeding for the first time the capacity of humans in this task. In this way, Artificial Intelligence gets its name because of the ability of electronic devices to solve problems that require intelligence and that, in the traditional way (programming), cannot be solved on a computer.

Looking back, the birth of Artificial Intelligence occurred more than 70 years ago, in the hands of Alan Turing, considered the father of this discipline. In 1936 he compiled a computational model, which consisted of heads on an infinite long band that could read, write and scroll. This computational model is called a Turing machine, and it is possible to represent any computational operation, in other words, any computation can be reduced and represented by a Turing Machine. This raises the following question: If the brain exerts computational operations, can it be reduced and represented by the Turing Machine?

If this is possible, it means that operations that we consider to be intelligent can be carried out automatically, such as the detection of objects in an image, the classification of sounds, or even the proper functioning of brain awareness. We cannot assert that the latter may occur, however, there is progress in simulating or approaching intelligent behavior, which is why subfields have emerged in the fields of Artificial Intelligence such as Computer Vision (CV), Machine Learning (ML) and Natural Language Processing (NLP).

The Japans case: Matchmaking

In Japan, a country with a high level of technological development, AI has played a more important role in matchmaking. AI devices began to be used to provide a picture of a potential partner only through the touch of a hand. It seems that the intelligence will be a determinant factor in matchmaking, today and in the future. You can find more information here. It seems that Artificial Intelligence begins to have the maturity to be applied in solving problems which until a few years ago were deemed unsolvable by machines. And one important factor in this phenomenon is that AI systems can operate independently like living things. This system is able to regulate itself, act like thinking beings, and thats why the reason is called Artificial Intelligence.

In short, Artificial Intelligence is the future of humans. We as humans cannot escape this reality. All we can do is make sure it works according to human norms and ethics. Hopefully this article can provide an overview of our future.

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The role of Artificial Intelligence today - - VENTS Magazine

Find the evolving relationship between big data and artificial intelligence. The growing popularity of these technologies offers engaging audience experience. It encourages newcomers to come up with an outstanding plan.

AI and Big Data help you transform your idea into substance. It helps you make full use of visuals, graphs, and multimedia to give your targeted audience with a great experience. According toMarkets And Markets, the worldwide market for AI in accounting assumed to grow. As a result, growth from $666 million in 2019 to $4,791 million by 2024.

The critical component of delivering an outstanding pitch is taking a step further with an incredible plan of assuring success. Big data and Artificial intelligence help you contribute to multiple industries bringing an effective plan. It can directly speak to investors and your targeted audience, covering essential aspects and representing your idea in a nutshell.

According to Techjury, The big data analytics market is set to reach $103 billion by 2023, and in 2019, the big data market is expected to grow by 20%.

From transformation to the phenomenal growth AI and Big data provide you with the accessibility of relevant information. Big data holds the data from multiple sources like social media platforms, search data, and others, which can be structured or unstructured. While artificial intelligence is intelligence demonstrated by machines with the rise of natural intelligence displayed by humans.

The most exciting thing for anyone to do is to identify the problem. So to know what prevents people from reaching their goal. From the product or service you wish to obtain the targeted audiences attention, it must solve the problem of the potential customers. There can be any problem from simple to complicated for which customers need a solution.

For every problem, there is a solution. Once you have understood the problem and willing to bring change, you can clearly solve the problem in the most defined ways. Artificial intelligence is a true reflection of technology advancement. With big data, you can make full use of vital information extracting the information you need.

For every problem, there is a solution. Once you have understood the problem and willing to bring change, you can clearly solve the problem in the most defined ways. Artificial intelligence is a true reflection of technology advancement. With big data, you can make full use of vital information extracting the information you need.

One can come with accurate solutions using AI and big data. It helps in introducing a low error rate compared to humans if appropriately coded. The AI takes the decision based on data and a set of algorithms, which decreases the chance of error. Big data and AI, when used together, can really help you solve the problem by answering the potential issues and bringing an effective solution.

To solve any kind of problem, one must know about the potential market. Divide your target market into segments from whom you expect to get a positive response. It helps you do what you need to. These advanced technologies have a strong foundation with outstanding capabilities to capture the potential market. One must learn and apply these technologies to get a better result in transforming the overall experience of customers.

Capturing the target audiences attention is as important as solving the problem. Once you know how big is your potential market is, and what your target audience wants, you can use these advanced technologies to pitch and get the desired result. That is only possible if you use your segment creatively and consider creating your own identity for targeting your customer while working on your business plan.

Every industry has its own competition with a particular set of competitors. One must invest in something that can really help people and bring the best solution for them with beneficial results and stand out in the real competition.

To stay in the market and promote your service, one must invest in providing customers with alternative solutions. These AI solutions can help you increase your customer base. Give your customers the reason to choose your solution over someone elses. That reason will be the identity that you will create in the market. Build a unique solution that can help you focus on growing your business and stay ahead in the competition.

Mark your presence in the market, accomplishing specific goals that you desire to achieve and have already accomplished. Make your business a reality setting realistic goals and perform better and notable milestones to achieve greater success. The core essence of running a smooth business and getting all that you desire is accomplishing set milestones.

Accomplishing set milestones can really help you get desired results and gain positive support from the trusted and reliable model. By doing this, you can strategies your small business plan with changing times and market demand. Gain an ideal position in the market with better results and in-depth data.

Achieving a milestone can be a tough task. However, with AI & Big data, it has become possible to get predictive analysis for better results and position of control. Consider all the options that make you stand out in the competition and help you grow your business.

AI can help you analyze consumer data patterns. It can predict what users would like to pay for with the help of big data. Both these technologies are compelling to present and provides a useful result that can boost your sales and increase business revenue.

Nitin Garg is the CEO and Co-founder of BR Softech Business Intelligence Software Company. Likes to share his opinions on IT industry via blogs. His interest is to write on the latest and advanced IT technologies which include IoT, VR & AR app development, web, and app development services.

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Evolving Relationship Between Artificial Intelligence and Big Data - ReadWrite

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2020 marks the start of a critical decade in achieving the United Nations Sustainable Development Goals (SDGs). As the Fourth Industrial Revolution continues to transform just about every industry sector, and revolutionize the way we live and work, it is only natural that we look to leverage its benefits to drive greater sustainability.

The role of artificial intelligence in achieving renewable energy and sustainable targets was highlighted in a recent report; using AI for environmental applications could add USD 5.2 trillion to the global economy, while reducing greenhouse gas emissions by 4 per cent, according to PwC.

The benefits of AI in sustainability are manifold: it can support the deployment of self-driving vehicles to transform transportation systems, or better manage food purchasing to reduce wastage. Smart grid technology aligns energy production, distribution and usage more effectively, while water wastage can be tackled at source.

However, while AI and other emerging technologies will undoubtedly prove vital in addressing sustainability challenges, we need to be careful about how they are applied if we dont want to waste an even more vital resource women.

AIs gender bias issues have also come under the spotlight recently from claims that Amazon had to abandon an experimental hiring tool because it discriminated against female applicants, to concerns about the (over) use of womens names for virtual assistants think Siri, and Alexa.

The benefits of AI in sustainability are manifold: it can support the deployment of self-driving vehicles to transform transportation systems, or better manage food purchasing to reduce wastage.

Possible reasons for this perceived gender bias are not hard to determine. While women make up half the global workforce, they only represent 30 per cent of tech industry employees, and less than 10 per cent of clean-tech start-ups are women-owned. In cutting-edge disciplines like AI, the imbalance is even worse: only 12 per cent of AI researchers, and just 6% of software developers, are female.

Given that SDG 5 achieving gender equality and empowering females is central to reaching all the other SDGs, clearly there is still much work to be done in the tech sector.

And the potential is there. In the workplace, technology can help us break down some of the barriers to female inclusion, such as inadequate training, limited access to financing, inflexible work environments, and lack of engagement with decision makers.

Nor is it necessarily true that women dont want to work in tech-related fields. Here in the UAE, women make up 56 per cent of graduates in STEM disciplines. Further, women make up around a third of tech entrepreneurs in the Middle East.

The UAE is also actively promoting opportunities for women in its emerging high-tech sector, and the investment is paying off. Around 40 per cent of the team working on the UAEs Mars Mission are female, while 90 per cent of the scientists at the Mohammed Bin Rashid Space Centre are women.

The UAEs commitment to parity between men and women has seen the country climb 23 positions in the UNDP Gender Equality Index in the past year, rising to 26th place and becoming the highest ranked Arab country in the world.

But this commitment to gender equality hasnt come at the expense of technology expertise the UAE also leads the region in technology adoption, and we are also one of the most advanced nations in the world in the application of AI. The UAE was the first country to appoint a minister of state for AI, while Masdar City is set to host the Mohamed Bin Zayed University of Artificial Intelligence the worlds first graduate-level AI research institution.

The role AI and digitalization can play in sustainability and gender parity will be discussed in-depth at Women in Sustainability, Environment and Renewable Energy (WiSER) Forum, convening on January 14th at this years Abu Dhabi Sustainability Week.

The UAEs progress in both gender equality and in driving technology adoption is a clear demonstration that applied correctly technology can be a vital tool in empowering women. By giving women and girls equal opportunities, we can maximize our collective potential. Just ask Siri.

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Artificial intelligence needn't be a barrier to gender parity if we use it smartly | Dr. Lamya Fawwaz - Gulf Today