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Searching video surveillance streaming for relevant information is a time-consuming mission that does not always convey accurate results. A new cloud-based deep-learning search engine augments surveillance systems with natural language search capabilities across recorded video footage.

The Ella search engine, developed by IC Realtime, uses both algorithmic and deep learning tools to give any surveillance or security camera the ability to recognize objects, colors, people, vehicles, animals and more.

It was designed with the technology backbone of Camio, a startup founded by ex-Googlers who realized there could be a way to apply search to streaming video feeds. Ella makes every nanosecond of video searchable instantly, letting users type in queries like “white truck” to find every relevant clip instead of searching through hours of footage. Ella quite simply creates a Google for video.

Traditional systems only allow the user to search for events by date, time, and camera type and to return very broad results that still require sifting, according to businesswire.com. The average surveillance camera sees less than two minutes of interesting video each day despite streaming and recording 24/7.

Ella instead does the work for users to highlight the interesting events and to enable fast searches of their surveillance and security footage. From the moment Ella comes online and is connected, it begins learning and tagging objects the cameras see.

The deep learning engine lives in the cloud and comes preloaded with recognition of thousands of objects like makes and models of cars; within the first minute of being online, users can start to search their footage.

Hardware agnostic, the technology also solves the issue of limited bandwidth for any HD streaming camera or NVR. Rather than push every second of recorded video to the cloud, Ella features interest-based video compression. Based on machine learning algorithms that recognize patterns of motion in each camera scene to recognize what is interesting within each scene, Ella will only record in HD when it recognizes something important. The uninteresting events are still stored in a low-resolution time-lapse format, so they provide 24×7 continuous security coverage without using up valuable bandwidth.

Ella works with both existing DIY and professionally installed surveillance and security cameras and is comprised of an on-premise video gateway device and the cloud platform subscription.

Source: This article was published i-hls.com

Published in Search Engine

FOR ALL THE hype about killer robots, 2017 saw some notable strides in artificial intelligence. A bot called Libratus out-bluffed poker kingpins, for example. Out in the real world, machine learning is being put to use improving farming and widening access to healthcare.

But have you talked to Siri or Alexa recently? Then you’ll know that despite the hype, and worried billionaires, there are many things that artificial intelligence still can’t do or understand. Here are five thorny problems that experts will be bending their brains against next year.

The meaning of our words

Machines are better than ever at working with text and language. Facebook can read out a description of images for visually impaired people. Google does a decent job of suggesting terse replies to emails. Yet software still can’t really understand the meaning of our words and the ideas we share with them. “We’re able to take concepts we’ve learned and combined them in different ways, and apply them in new situations,” says Melanie Mitchell, a professor at Portland State University. “These AI and machine learning systems are not.”

Mitchell describes today’s software as stuck behind what mathematician Gian Carlo-Rota called “the barrier of meaning.” Some leading AI research teams are trying to figure out how to clamber over it.

One strand of that work aims to give machines the kind of grounding in common sense and the physical world that underpins our own thinking. Facebook researchers are trying to teach software to understand reality by watching the video, for example. Others are working on mimicking what we can do with that knowledge about the world. Google has been tinkering with software that tries to learn metaphors. Mitchell has experimented with systems that interpret what’s happening in photos using analogies and a store of concepts about the world.

The reality gap impeding the robot revolution

Robot hardware has gotten pretty good. You can buy a palm-sized drone with HD camera for $500. Machines that haul boxes and walk on two legs have improved also. Why are we not all surrounded by bustling mechanical helpers? Today’s robots lack the brains to match their sophisticated brawn.

Getting a robot to do anything requires specific programming for a particular task. They can learn operations like grasping objects from repeated trials (and errors). But the process is relatively slow. One promising shortcut is to have robots train in virtual, simulated worlds, and then download that hard-won knowledge into physical robot bodies. Yet that approach is afflicted by the reality gap—a phrase describing how skills a robot learned in simulation do not always work when transferred to a machine in the physical world.

The reality gap is narrowing. In October, Google reported promising results in experiments where simulated and real robot arms learned to pick up diverse objects including tape dispensers, toys, and combs.

Further progress is important to the hopes of people working on autonomous vehicles. Companies in the race to roboticize driving deploy virtual cars on simulated streets to reduce the time and money spent testing in real traffic and road conditions. Chris Urmson, CEO of autonomous-driving startup Aurora, says making virtual testing more applicable to real vehicles is one of his team’s priorities. “It’ll be neat to see over the next year or so how we can leverage that to accelerate learning,” says Urmson, who previously led Google parent Alphabet’s autonomous-car project.

Guarding against AI hacking

The software that runs our electrical gridssecurity cameras, and cell phones is plagued by security flaws. We shouldn’t expect software for self-driving cars and domestic robots to be any different. It may, in fact, be worse: There’s evidence that the complexity of machine-learning software introduces new avenues of attack.

Researchers showed this year that you can hide a secret trigger inside a machine-learning system that causes it to flip into evil mode at the sight of a particular signal. The team at NYU devised a street-sign recognition system that functioned normally—unless it saw a yellow Post-It. Attaching one of the sticky notes to a stop sign in Brooklyn caused the system to report the sign as a speed limit. The potential for such tricks might pose problems for self-driving cars.

The threat is considered serious enough that researchers at the world’s most prominent machine-learning conference convened a one-day workshop on the threat of machine deception earlier this month. Researchers discussed fiendish tricks like how to generate handwritten digits that look normal to humans but appear as something different to software. What you see as a 2, for example, a machine vision system would see as a 3. Researchers also discussed possible defenses against such attacks—and worried about AI being used to fool humans.

Tim Hwang, who organized the workshop, predicted using the technology to manipulate people is inevitable as machine learning becomes easier to deploy, and more powerful. “You no longer need a room full of PhDs to do machine learning,” he said. Hwang pointed to the Russian disinformation campaign during the 2016 presidential election as a potential forerunner of AI-enhanced information war. “Why wouldn’t you see techniques from the machine learning space in these campaigns?” he said. One trick Hwang predicts could be particularly effective is using machine learning to generate fake video and audio.

Graduating beyond boardgames

Alphabet’s champion Go-playing software evolved rapidly in 2017. In May, a more powerful version beat Go champions in China. Its creators, research unit DeepMind, subsequently built a version, AlphaGo Zero, that learned the game without studying human play. In December, another upgrade effort birthed AlphaZero, which can learn to play chess and Japanese board game Shogi (although not at the same time).

That avalanche of notable results is impressive—but also a reminder of AI software’s limitations. Chess, Shogi, and Go are complex but all have relatively simple rules and gameplay visible to both opponents. They are a good match for computers’ ability to rapidly spool through many possible future positions. But most situations and problems in life are not so neatly structured.

That’s why DeepMind and Facebook both started working on the multiplayer video game StarCraft in 2017. Neither have yet gotten very far. Right now, the best bots—built by amateurs—are no match for even moderately-skilled players. DeepMind researcher Oriol Vinyals told WIREDearlier this year that his software now lacks the planning and memory capabilities needed to carefully assemble and command an army while anticipating and reacting to moves by opponents. Not coincidentally, those skills would also make software much better at helping with real-world tasks such as office work or real military operations. Big progress on StarCraft or similar games in 2018 might presage some powerful new applications for AI.

Teaching AI to distinguish right from wrong

Even without new progress in the areas listed above, many aspects of the economy and society could change greatly if existing AI technology is widely adopted. As companies and governments rush to do just that, some people are worried about accidental and intentional harms caused by AI and machine learning.

How to keep the technology within safe and ethical bounds was a prominent thread of discussion at the NIPS machine-learning conference this month. Researchers have found that machine learning systems can pick up unsavory or unwanted behaviors, such as perpetuating gender stereotypes, when trained on data from our far-from-perfect world. Now some people are working on techniques that can be used to audit the internal workings of AI systems, and ensure they make fair decisions when putting to work in industries such as finance or healthcare.

The next year should see tech companies put forward ideas for how to keep AI on the right side of humanity. Google, Facebook, Microsoft, and others have begun talking about the issue, and are members of a new nonprofit called Partnership on AI that will research and try to shape the societal implications of AI. Pressure is also coming from more independent quarters. A philanthropic project called the Ethics and Governance of Artificial Intelligence Fund is supporting MIT, Harvard, and others to research AI and the public interest. A new research institute at NYU, AI Now, has a similar mission. In a recent report, it called for governments to swear off using “black box” algorithms not open to public inspection in areas such as criminal justice or welfare.

Source: This article was published wired.com By Tom

Published in Science & Tech

Google has officially announced that it is opening an AI center in Beijing, China.

The confirmation comes after months of speculation fueled by a major push to hire AI talent inside the country.

Google’s search engine is blocked in China, but the company still has hundreds of staff in China which work on its international services. In reference to that workforce, Alphabet chairman Eric Schmidt has said the company “never left” China, and it makes sense that Google wouldn’t want to ignore China’s deep and growing AI talent pool, which has been hailed by experts that include former Google China head Kaifu Lee.

Like the general talent with Google China, this AI hiring push isn’t a sign that Google will launch new services in China. Although it did make its Google Translate app available in China earlier this year in a rare product move on Chinese soil.

Instead, the Beijing-based team will work with AI colleagues in Google offices across the world, including New York, Toronto, London and Zurich.

“I believe AI and its benefits have no borders. Whether a breakthrough occurs in Silicon Valley, Beijing or anywhere else, it has the potential to make everyone’s life better. As an AI first company, this is an important part of our collective mission. And we want to work with the best AI talent, wherever that talent is, to achieve it,” wrote Dr. Fei-Fei Li, Chief Scientist at Google Cloud, in a blog post announcing plans for the China lab.

Related...

Li, formerly the director of Stanford University’s Artificial Intelligence Lab, was a high-profile arrival when she joined Google one year ago. She will lead the China-based team alongside Jia Li, who was hired from Snap where she had been head of research at the same time as Li.

The China lab has “already hired some top talent” and there are currently more than 20 jobs open, according to a vacancy listing.

“Besides publishing its own work, the Google AI China Center will also support the AI research community by funding and sponsoring AI conferences and workshops, and working closely with the vibrant Chinese AI research community,” Li added.

Google is up against some tough competitors for talent. Aside from the country’s three largest tech companies Baidu, Tencent and Alibaba, ambitious $30 billion firm Bytedance — which acquired Musical.ly for $1 billion — and fast-growing companies SenseTime and Face++ all compete for AI engineers with compensation deals growing higher.

Source: This article was published techcrunch.com By Jon Russell

Published in Online Research

Response:now uses machine learning to save companies time and money with automatically produced, actionable research insights.

We’re hearing about a lot of companies using artificial intelligence (AI) to make the most of the data they collect. Now market research has adopted the technology.

After finding success with clients such as Google and Mastercard abroad, Prague-based response:now is bringing their AI-powered app to the United States.

The company now offers a fully self-service, programmatic platform that creates research reports based on machine learning. Then it uses a human editor to tease out any undetected nuances and reconcile any disparities.

Fred Barber, newly appointed managing director of response:now in North America says it makes sense to use AI in research.

Since research is essentially data — and common metrics such as the Net Promoter score are basically formulas — an algorithm can learn to make reasonable assumptions and conclusions about the data it analyzes, Barber said. Using AI, response:now automatically creates reports, cutting down much of the time spent in traditional research environments.

According to Barber, 75-80 percent of the work effort in market research is in the writing of the reports. “It’s costly and time-consuming,” Barber said. In comparison to traditional research, response:now can deliver in “five days, not five weeks and for 2K instead of $20 (on average).”

In many cases, Barber says, the company can provide research at three times the speed and one-third the cost of current market research and DIY offerings.

Related...

The company enables their clients to get research on a wide variety of variables, including ad performance, packaging design, customer satisfaction and more.

“We’ve enabled market research to become a much more ubiquitous part of the business process,” Barber said.

Source: This article was published martechtoday.com By Robin Kurzer

Published in Market Research

Queries provide data mine for Microsoft's AI developments

Microsoft's Bing search engine has long been a punch line in the tech industry, an also-ran that has never come close to challenging Google's dominant position.

But Microsoft could still have the last laugh, since its service has helped lay the groundwork for its burgeoning artificial intelligence effort, which is helping keep the company competitive as it builds out its post-PC future.

Bing probably never stood a chance at surpassing Google, but its 2nd-place spot is worth far more than the advertising dollars it pulls in with every click. Billions of searches over time have given Microsoft a massive repository of everyday questions people ask about their health, the weather, store hours or directions.

“The way machines learn is by looking for patterns in data,” said former Microsoft CEO Steve Ballmer, when asked earlier this year about the relationship between Microsoft's AI efforts and Bing, which he helped launch nearly a decade ago. “It takes large data sets to make that happen.”

Microsoft has spent decades investing in various forms of artificial intelligence research, the fruits of which include its voice assistant Cortana, email-sorting features and the machine-learning algorithms used by businesses that pay for its cloud platform Azure.

It's been stepping up its overt efforts recently, such as with this year's acquisition of Montreal-based Maluuba, which aims to create “literate machines” that can process and communicate information more like humans do.

Some see Bing as the overlooked foundation to those efforts.

“They're getting a huge amount of data across a lot of different contexts – mobile devices, image searches,” said Larry Cornett, a former executive for Yahoo's search engine. “Whether it was intentional or not, having hundreds of millions of queries a day is exactly what you need to power huge artificial intelligence systems.”

Bing started in 2009, a rebranding of earlier Microsoft search engines. Yahoo and Microsoft signed a deal for Bing to power Yahoo's search engine, giving Microsoft access to Yahoo's greater search share, said Cornett, who worked for Yahoo at the time. Similar deals have infused Bing into the search features for Amazon tablets and, until recently, Apple's Siri.

All of this has helped Microsoft better understand language, images and text at a large scale, said Steve Clayton, who as Microsoft's chief storyteller helps communicate the company's AI strategy.

“It's so much more than a search engine for Microsoft,” he said. “It's fuel that helps build other things.”

Bing serves dual purposes, he said, as a source of data to train artificial intelligence and a vehicle to be able to deliver smarter services.

While Google also has the advantage of a powerful search engine, other companies making big investments in the AI race – such as IBM or Amazon – do not.

“Amazon has access to a ton of e-commerce queries, but they don't have all the other queries where people are asking everyday things,” Cornett said.

Neither Bing nor Microsoft's AI efforts have yet made major contributions to the company's overall earnings, though the company repeatedly points out “we are infusing AI into all our products,” including the workplace applications it sells to corporate customers.

The company on Thursday reported fiscal first-quarter profit of $6.6 billion, up 16 percent from a year earlier, on revenue of $24.5 billion, up 12 percent. Meanwhile, Bing-driven search advertising revenue increased by $210 million, or 15 percent, to $1.6 billion – or roughly 7 percent of Microsoft's overall business.

That's OK by current Microsoft current CEO Satya Nadella, who nearly a decade ago was the executive tapped by Ballmer to head Bing's engineering efforts.

In his recent autobiography, Nadella describes the search engine as a “great training ground for building the hyper-scale, cloud-first services” that have allowed the company to pivot to new technologies as its old PC-software business wanes.

Source: This article was published journalgazette.net By MATT O'BRIEN

Published in Search Engine

The CIA is developing AI to advance data collection and analysis capabilities. These technologies are, and will continue to be, used for social media data.

INFORMATION IS KEY

The United States Central Intelligence Agency (CIA) requires large quantities of data, collected from a variety of sources, in order to complete investigations. Since its creation in 1947, intel has typically been gathered by hand. The advent of computers has improved the process, but even more modern methods can still be painstakingly slow. Ultimately, these methods only retrieve minuscule amounts of data when compared what artificial intelligence (AI) can gather.

According to information revealed by Dawn Meyerriecks, the deputy director for technology development with the CIA, the agency currently has 137 different AI projects underway. A large portion of these ventures are collaborative efforts between researchers at the agency and developers in Silicon Valley. But emerging and developing capabilities in AI aren’t just allowing the CIA more access to data and a greater ability to sift through it. These AI programs have taken to social media, combing through countless public records (i.e. what you post online). In fact, a massive percentage of the data collected and used by the agency comes from social media. 

As you might know or have guessed, the CIA is no stranger to collecting data from social media, but with AI things are a little bit different, “What is new is the volume and velocity of collecting social media data,” said Joseph Gartin, head of the CIA’s Kent School. And, according to Chris Hurst, the chief operating officer of Stabilitas, at the Intelligence Summit, “Human behavior is data and AI is a data model.”

AUTOMATION

According to Robert Cardillo, director of the National Geospatial-Intelligence Agency, in a June speech, “If we were to attempt to manually exploit the commercial satellite imagery we expect to have over the next 20 years, we would need eight million imagery analysts.” He went on to state that the agency aims to use AI to automate about 75% of the current workload for analysts. And, if they use self-improving AIs as they hope to, this process will only become more efficient.

While countries like Russia are still far behind the U.S. in terms of AI development, especially as it pertains to intelligence, there seems to be a global push — if not a race — forward.  Knowledge is power, and creating technology capable of extracting, sorting, and analyzing data faster than any human or other AI system could is certainly sounds like a fast track to the top.  As Vladimir Putin recently stated on the subject of AI, “Whoever becomes the leader in this sphere will become the ruler of the world.”

Source: This article was published futurism.com By Chelsea Gohd

Published in Science & Tech

From The Terminator to Blade Runner, pop culture has always leaned towards a chilling depiction of artificial intelligence (AI) and our future with AI at the helm. Recent headlines about Facebook panicking because their AI bots developed a language of their own have us hitting the alarm button once again. Should we really feel unsettled with an AI future?

News flash: that future is here. If you ask Siri, the helpful assistant who magically lives inside your phone, to read text messages and emails to you, find the nearest pizza place or call your mother for you, then you’ve made AI a part of your everyday life. Even current weather forecasting systems, spam filtering programs, and Google’s search engine – among so many other practical applications – are AI-powered. Now, artificial intelligence doesn’t seem that alarming, right?

What Is Artificial Intelligence?

AI refers to machine intelligence or a machine’s ability to replicate the cognitive functions of a human being. It has the ability to learn and solve problems. In computer science, these machines are aptly called “intelligent agents” or bots.

Not all AI are alike. In fact, what is considered artificial intelligence has shifted as the technology develops. Today, there are three recognized levels in the AI spectrum, all of which we can experience today.

Assisted intelligence – This refers to the automation of basic tasks. Examples include machines in assembly lines.

Augmented intelligence – There is a give and take with augmented intelligence. An AI learns from human input. We, in turn, can make more accurate decisions based on AI information. As Anand Rao of PricewaterhouseCoopers (PwC) Data & Analytics puts it: “There is symmetry with augmented intelligence.”

Autonomous intelligence – This is AI with humans out of the loop. Think self-driving cars and autonomous robots.

Deep Learning

It is actually just in recent years when a good number of scientists and innovators began to devote their work to artificial intelligence. Technology has finally caught up with faster and more powerful GPUs. Industry observers tack this resurgence to 2015, when fast and powerful parallel processing became accessible. This is also around the birth of the so-called Big Data movement, when it became possible to store and analyze infinite amounts of data.

Thus, we reach today, the era of Deep Learning. Deep learning pertains to the use of artificial neural networks (ANNs) in order to facilitate learning at multiple layers. It is a part of machine learning based on how data is presented, instead of task-based algorithms.

Deep learning has led the way in revolutionizing analytics and enabling practical applications of AI.

We see it in something as basic as automatic photo-tagging on Facebook, a process developed by Yann LeCun for the company in 2013. Blippar, on the other hand, has come out with an augmented reality application that employs deep learning in real-time object recognition in 2015.

You can look forward to driverless cars and so much more. In the same we, we can expect AI to be applied further in business, particularly in decision-making.

Artificial Intelligence in Business

According to Dr. John Kelly III, IBM Senior Vice President for Research and Solutions: “The success of cognitive computing will not be measured by Turing tests or a computer’s ability to mimic humans. It will be measured in more practical ways, like return on investment, new market opportunities, diseases cured and lives saved.”

Yes, AI technology isn’t the end but only a means towards effectiveness and efficiency, improved innovative capabilities, and better opportunities. And, we’ve seen this in several industries that have begun to adopt AI into their operations.

According to a survey by Tech Pro Research, up to 24 percent of businesses currently implement or plan on using artificial intelligence. Stand-outs are in the health, financial services and automotive sectors.

In financial services, PwC has put together massive amounts of data from the US Census Bureau, US financial data, and other public licensed sources to create $ecure, a large-scale model of 320 million US consumers’ financial decisions. The model is designed to help financial services companies map buyer personas, simulate “future selves” and anticipate customer behavior. It has enabled these financial services companies in validating real-time business decisions within seconds.

The automotive industry, on the other hand, has developed several AI applications, from vehicle design to marketing and sales decision-making support. For instance, artificial intelligence has led to the design of smarter (even driverless) cars, equipped with multiple sensors that learn and identify patterns. This is put to use through add-on safe-drive features that warn drivers of possible collisions and lane departures.

Like in the financial services sector, AI is used to develop a model of the automobile ecosystem. Here, you have bots that map the decisions made from automotive players, such as car buyers and manufacturers, and transportation services providers. This has helped companies predict the adoption of electric and driverless vehicles, and the implementation of non-restrictive pricing schemes that work on their target market. It has also helped them make better advertising decisions.

The key here is how artificial intelligence systems are able to run more than 200,000 GTM (go-to-market) scenarios, instead of just a typical handful. What you get is optimized scenarios that maximize revenues.

It’s a similar case in the fields of retail, marketing and sales. According to Adobe Marketing Cloud Product Manager, John Bates: “For retail companies that want to compete and differentiate their sales from competitors, retail is a hotbed of analytics and machine learning.” AI application development has provided marketers with new and more reliable tools in market forecasting, process automation and decision-making.

AI and Business Decisions

Prior to the resurgence of AI and its eventual commercial application, executives have had to rely on inconsistent and incomplete data. With artificial intelligence, they have data-based models and simulations to turn to.

According to PwC’s Rao, limitless outcome modeling is one of the breakthroughs in today’s AI systems. He reiterates: “There’s an immense opportunity to use AI in all kinds of decision making”

Today’s AI systems start from zero and feed on a regular diet of big data. This is augmented intelligence in action, which eventually provides executives with sophisticated models as basis for their decision-making.

There are several AI applications that enhance decision-making capacities. Here are some of them:

Marketing Decision-Making with AI

There are many complexities to each marketing decision. One has to know and understand customer needs and desires, and align products to these needs and desires. Likewise, having a good grasp of changing consumer behavior is crucial to making the best marketing decisions, in the short- and long-run.

AI modeling and simulation techniques enable reliable insight into your buyer personas. These techniques can be used to predict consumer behavior. Through a Decision Support System, your artificial intelligence system is able to support decisions through real-time and up-to-date data gathering, forecasting, and trend analysis.

Customer Relationship Management (CRM)

Artificial intelligence within CRM systems enable its many automated functions, such as contact management, data recording and analyses and lead ranking. AI’s buyer persona modeling can also provide you with a prediction of a customer’s lifetime value. Sales and marketing teams can work more efficiently through these features.

Recommendation System

Recommendation systems were first implemented in music content sites. This has since been extended to different industries. The AI system learns a user’s content preferences and pushes content that fit those preferences. This can help you reduce bounce rate. Likewise, you can use the information learned by your AI to craft better-targeted content.

Expert System

Artificial intelligence has tried to replicate the knowledge and reasoning methodologies of experts through Expert System, a type of problem-solving software. Expert systems, such as MARKEX (for marketing), apply expert thinking processes to provided data. Output includes assessment and recommendations for your specific problem.

Automation Efficiency and AI

The automation efficiency lent by artificial intelligence to today’s business processes has gone beyond the assembly lines of the past. In several business functions, such as marketing and distribution, AI has been able to hasten processes and provide decision-makers with reliable insight.

In marketing, for instance, the automation of market segmentation and campaign management has enabled more efficient decision-making and quick action. You get invaluable insight on your customers, which can help you enhance your interactions with them. Marketing automation is one of the main features of a good CRM application.

Distribution automation with the help of AI has also been a key advantage of several retailers. Through AI-supported monitoring and control, retailers can accurately predict and respond to product demand.

An example is the online retail giant, Amazon. In 2012, it acquired Kiva Systems, which developed warehouse robots. Since its implementation, Kiva robots have been tasked with product monitoring and replenishment, and order fulfillment. They can even do the lifting for you. That’s a big jump in Amazon efficiency, compared to the time when humans had to do the grunt work.

Social Computing

Social computing helps marketing professionals understand the social dynamics and behaviors of a target market. Through AI, they can simulate, analyze and eventually predict consumer behavior. These AI applications can also be used to understand and data-mine online social media networks.

Opinion Mining

Opinion mining is a kind of data mining that searches the web for opinions and feelings. It is a way for marketers to know more about how their products are received by their target audience. Manual mining and analyses require long hours. AI has helped shorten this through reliable search and analyses functions.

This form of AI is often used by search engines, which regularly rank people’s interests in specific web pages, websites and products. These bots employ different algorithms to get to a target’s HITS and PageRank, among other online scoring systems. Here, hyperlink-based AI is employed, wherein bots seek out clusters of linked pages and see these as a group sharing a common interest.

The Future of Business Decision-Making With AI

With no Terminator or Replicant looming in the periphery, there really is no danger to artificial intelligence, only potential. Arguably, there shouldn’t even be the more practical scare of losing people’s jobs to machines. Experts say that AI can actually enhance people’s jobs and allows them to work more efficiently.

And surely, this rings true with respect to decision-making. When decision-makers and business executives have reliable data analyses, recommendations and follow-ups through artificial intelligence systems, they can make better choices for their business and employees. You don’t just enhance the work of individual team members. AI also improves the competitive standing of the business.

The gap lies in developing artificial intelligence systems that could deal with the enormous amount of data currently available. According to Gartner, a marketing research organization, today’s data is set to balloon to up to 800% by 2020. With this, you get about 80% of unstructured data, made up of images, emails, audio clips and the like. At this point, there is nothing – neither human nor artificial intelligence – that can sift through this amount of data, in order to make it usable for business.

According to IBM’s Dr. Kelly: ““This data represents the most abundant, valuable and complex raw material in the world. And until now, we have not had the means to mine it.” He believes that it is companies involved in genomics and oil that will find the means to mine this resource.

He delves further on the future of AI and analytics: “In the end, all technology revolutions are propelled not just by discovery, but also by business and societal need. We pursue these new possibilities not because we can, but because we must.”

Source: This article was published business2community By Dan Sincavage

Published in Business Research

Scientists from Queen Mary University of London (QMUL) have created an artificial intelligence (AI) that uses internet searches to help co-design a word association magic trick.

The computer automatically sources and processes associated words and images required for the novel mind reading card trick which is performed by a magician.

Previously psychological experiments on participants would need to have been carried out by the magician to reveal how the human mind associates certain words and images, but the AI can complete the same job by searching through the internet.

The computer is able to assist in a creative task by taking over some of the workload during the design of the trick and by acting as an aid to prompt further creativity as it can uncover suggestions the magician may not have considered.

The researchers hope this study will introduce the use of computer technology as a natural language data sourcing and processing tool for magic trick design purposes.

Professor Peter McOwan from QMUL's School of Electronic Engineering and Computer Science, and co-author of the study, said: "This research is important, as it provides further evidence that computers can be used as aids in creative tasks. Particularly, it contributes to the relatively new field of the science of magic, placing magic in a similar research realm to music and other arts, and worth of investigation and exploration on its own terms."

He added: "New magic tricks are constantly being created. This research provides the magic community with another tool to use to this end, and the scientific community with some further insight into the possible uses and implications of applied computational creativity."

The trick is performed with a set of custom playing cards consisting of matching words and images supplied by the computer. During the performance the spectator chooses from two shuffled decks an image card and a word card that form a good match, which the performer can predict thanks to the mathematical properties of a deck of cards and the way the human mind makes mental associations.

Though the algorithm can replace the need for carrying out psychological experiments on volunteers to help determine the mind associations required for the trick, the researchers found that to produce the best results a combination of the algorithm and psychological experiments was ideal.

Similarly the matches of words and images suggested by the algorithm would need to be filtered by the magician before they could be used in the trick.

Dr Howard Williams, co-author of the paper, said: "The association trick is still very much the result of a human creative act, though a computer now stands in as a significant proxy for some of the process.

"Overall, the effect for the spectators is magical, and has been brought about by the blending of human and computational design processes."

Source: This article was published eurekalert.org

Published in Online Research

Amigo, a white robot the size of a person, uses information gathered by other robots to move towards a table to pick up a carton of milk and deliver it to an imaginary patient in a mock hospital room at the Technical University of Eindhoven, Netherlands, Wednesday Jan. 15, 2014. A group of five of Europe’s top technical universities, together with technology conglomerate Royal Philips NV, are launching an open-source system dubbed “RoboEarth” Thursday. The heart of the mission is to accelerate the development of robots and robotic services via efficient communication with a network, or “cloud”. AP

VANCOUVER, Canada — Intelligent machines of the future will help restore memory, mind your children, fetch your coffee and even care for aging parents.

It will all be part of a brave new world of the not-so-distant future, in which innovative smart machines, rather than being the undoing of people — as some have long feared — actually enhance humans.

That was the vision outlined at the prestigious TED Conference by experts who say technology will allow people to take on tasks they might only have dreamed of in the past.

“Super-intelligence should give us super-human abilities,” Tom Gruber, head of the team responsible for Apple’s Siri digital assistant, said during an on-stage talk Tuesday night.

Smarter machines, smarter humans

“As machines get smarter, so do we,” Gruber said.

“Artificial intelligence can enable partnerships where each human on the team is doing what they do best,” he told the popular technology conference.

Gruber, a co-creator of Siri and artificial intelligence research at Apple, told of being drawn to the field three decades ago by the potential for technology to meet people’s needs.

“I am happy to see that the idea of an intelligent personal assistant is mainstream,” he said.

Now he has set his sights on smart machines, and is turning the thinking about the technology on its head.

“Instead of asking how smart we can make our machines, let’s ask how smart our machines can make us,” Gruber said.

Already smart personal assistants are taking hold, pioneered by the likes of Apple’s Siri.

South Korean giant Samsung created Bixby to break into the surging market for voice-activated virtual assistants, which includes Amazon’s Alexa, Google’s Assistant and Microsoft’s Cortana.

Amazon appears to have impacted the sector the most with its connected speakers using Alexa. The service allows users a wide range of voice interactions for music, news, purchases and connects with smart home devices.

Remembering everything

Gruber envisions artificial intelligence — AI — getting even more personal, perhaps augmenting human memory.

“Human memory is famously flawed — like, where did the 1960s go and can I go there too?” Gruber quipped.

He spoke of a future in which artificial intelligence remembers everyone met during a lifetime and details of everything someone read, heard, said or did.

“From the tiniest clue it could help you retrieve anything you’ve seen or heard before,” he said.

“I believe AI will make personal memory enhancement a reality; I think it’s inevitable.”

Such memories would need to be private, with people choosing what to keep, and be kept absolutely secure, he maintained.

Surefooted robots

Boston Dynamics robotics company founder Marc Raibert was at TED with a four-legged SpotMini robot nimble enough to frolic amid the conference crowd.

He smiled but would not comment when asked by AFP about the potential to imbue the gadget with the kind of artificial intelligence described by fellow speakers.

Raibert did, however, note that the robots are designed to be compatible with new “user interfaces.”

Current virtual assistants have been described as a step into an era of controlling computers by speaking instead of typing or tapping screens.

“I think it won’t be too long before we’re using robots to take care of our parents, or help our children take care of us,” Raibert said.

The ‘gorilla problem

Not everyone at TED embraced the idea of a future in which machines are smarter and more capable than humans, however.

Stuart Russell, a University of California at Berkeley computer sciences professor, referred to the situation as the “gorilla problem” in that when smarter humans came along it boded ill for evolutionary ancestors.

“This queasy feeling that making something smarter than your own species is not a good idea,” said Russell, co-author of the book Artificial Intelligence: A Modern Approach.

As an AI researcher he supported research in the technology.

Russell now advocates programming machines with robotic laws to govern their behavior — and ensure cannot end up working against human interests.

He gave the example of a robot being told to simply fetch coffee.

A machine not constrained by proper principles might decide that accomplishing the task required it to defend against being shut down and remove all obstacles from its path by whatever means necessary.

Russell counseled robot principles including altruism, humility, and making a priority of human values.

“You are probably better off with a machine that is like this,” Russell said.

“It is a first step in human compatibility with AI.” CBB

This article was  published in technology.inquirer.net

Published in Science & Tech

Artificial intelligence has made great progress in helping computers recognize images in photos and recommending products online that you're more likely to buy. But the technology still faces many challenges, especially when it comes to computers remembering things like humans do.

On Tuesday, Apple’s director of AI research, Ruslan Salakhutdinov, discussed some of those limitations. However, he steered clear during his talk at an MIT Technology Review conference of how his secretive company incorporates AI into its products like Siri.

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Salakhutdinov, who joined Apple in October, said he is particularly interested in a type of AI known as reinforcement learning, which researchers use to teach computers to repeatedly take different actions to figure out the best possible resultGoogle (GOOG, +0.17%), for example, used reinforcement learning to help its computers find the best possible cooling and operating configurations in its data centers, thus making them more energy efficient.

Researchers at Carnegie Mellon, where Salakhutdinov is also an associate professor, recently used reinforcement learning to train computers to play the 1990's era video game Doom, Salakhutdinov explained. Computers learned to quickly and accurately shoot aliens while also discovering that ducking helps with avoiding enemy fire. However, these expert Doom computer systems are not very good at remembering things like the maze's layouts, which keeps them from planning and building strategies, he said.

Part of Salakhutdinov’s research involves creating AI-powered software that memorizes the layouts of virtual mazes in Doom and points of references in order to locate specific towers. During the game, the software first spots what's either a red or green torch, with the color of the torch corresponding to the color of the tower it needs to locate.

Eventually, the software learned to navigate the maze to reach the correct tower. When it discovered the wrong tower, the software backtracked through the maze to find the right one. What was especially noteworthy was that the software was able to recall the color of the torch each time it spotted a tower, he explained.

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However, Salakhutdinov said this type of AI software takes “a long time to train” and that it requires enormous amounts of computing power, which makes it difficult to build at large scale. “Right now it’s very brittle,” Salakhutdinov said.

Another area Salakhutdinov wants to explore is teaching AI software to learn more quickly from “few examples and few experiences.” Although he did not mention it, his idea would benefit Apple in its race to create better products in less time.

Some AI experts and analysts believe Apple's AI technologies are inferior to competitors like Google or Microsoft because of the company's stricter user privacy rules, which limits the amount of data it can use to train its computers. If Apple used less data for computer training, it could perhaps satisfy its privacy requirements while still improving its software as quickly as rivals.

Author : Jonathan Vanian

Source : fortune.com

Published in Others
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