Category Archives: Artificial Intelligence

DHAKA (THE DAILY STAR/ASIA NEWS NETWORK) - Nobel Prize-winning author Kazuo Ishiguro's latest novel Klara And The Sun, his first since receiving the award in literature in 2017, has some relevance for policymakers and ordinary citizens across the globe.

The main protagonist in this dystopian science fiction story is Klara, an artificial friend (AF) - a human-like teenager who behaves and thinks almost like her cohort of the same age and is a fast learner, as any device or robot using artificial intelligence (AI) can be expected to be.

However, what we also learn is that if robots, even if they are super-intelligent, are allowed to make decisions that affect the lives of humans, it might lead to unintended consequences unless there are strict guidelines protecting privacy and other individual rights.

Many discerning readers might already be aware that AI is whipping up quite a storm, particularly as it makes inroads into facial recognition software, law enforcementand hiring decisions in the corporate world.

Policymakers in many countries are alarmed, realising the pros as well as the cons of this revolutionary technology.

The European Union, which has been at the forefront of safeguarding privacy rights, unveiled strict regulations last month to govern the use of AI, a first-of-its-kind policy that outlines how companies and governments can use AI technology, also known as "machine learning".

While these new rules are yet to be implemented and are still on the drawing boards, their impact will be similar to that of the General Data Protection Regulation (GDPR), which was drawn up to keep global technology companies such as Amazon, Google, Facebook and Microsoft in check.

The EU has been the world's most aggressive watchdog of the technology industry, with its policies often used as blueprints by other nations.

As mentioned, the bloc has already enacted the world's most far-reaching data privacy regulation, the GDPR, and is debating additional antitrust and content-moderation laws.

"We have to be aware that GDPR is not made for blockchain, facial or voice recognition, text and data mining... artificial intelligence," said Mr Axel Voss, a German member of the European Parliament and one of the creators of GDPR.

AI, in simple terms, is a computer programthat enables the machine to learn how to mimic the problem-solving and decision-making capabilities of the human mind.

An AI-enabled device learns how to respond to certain situations and uses algorithms and historical data to recognise a face, predict the weather or support a search engine like Google.

AI is playing a critical role in autonomous vehicles (such as drones and self-driving cars), medical diagnosis, creating art, playing games (such as chess or Go), search engines, online assistants (such as Siri), image recognition in photographs, spam filtering, predicting flight delays and much more.

Large technology companies have poured billions of dollars into developing AI, as also have scores of others that use it to develop medicine, underwrite insurance policies and judge creditworthiness.

Governments use versions of AI in criminal justice and in allocating public services like income support.

The potential for AI is enormous, and here is the rub. To what extent can machines be manipulated to gain financial or strategic advantage by those who fund this research or manufacture them?

Are they really neutral? Can machines adapt to the humans they interactwith, or do existing biases become hardwired?

Facial recognition algorithms have been at the centre of privacy and ethics debates. Imagine a scenario where a government buys facial recognition software and uses it to track attendees during protest marches.

In Hong Kong, the police used a system, created by Israeli company Cellebrite, to access the phones of 4,000 protesters.

A researcher at an AI global conference claimed to be able to generate faces - including aspects of a person's age, gender and ethnicity - based on voices.

Digital rights groups across the United States, Britain and the EU have already raised many issues prompted by advances in AI research, and these include privacy violations, ethical concernsand lack of human control over AI.

Ms Ria Kalluri, a machine-learning scientist at Stanford University in California, said: "Because training data are drawn from human output, AI systems can end up mimicking and repeating human biases, such as racism and sexism."

Ms Kalluri urged her colleagues to dedicate more efforts into tackling scientific questions that make algorithms more transparent and create ways for non-experts to challenge a model's inner workings.

This controversy brings to the fore the role humans play in training the machines. We each have our biases and preferences, and the machines may inherit them.

There is increasing support to "debias the algorithms".

University College Dublin cognitive scientistAbeba Birhane, citing uses of AI in hiring and surveillance, said:"Algorithms exclude older workers, trans people, immigrants, children."

AI had already evoked criticisms from the late Professor Stephen Hawking, one of Britain's pre-eminent scientists, and billionaire entrepreneur Elon Musk has also raised a red flag.

Having said that, it cannot be gainsaid that the AI revolution is here and has come to stay.

The important question is, how do we harness the power of AI and manage its negative influence?

On the positive side, research has conclusively proven that AI is at least as good as humans in determining medical diagnosis.

However, rights groups have argued that "people should be told when their medical diagnosis comes from AI and not a human doctor." And using the same logic, one could propose that the same warning be issued if, for example, advertising or political speech is AI-generated.

Coming back to Klara And The Sun, Henry Capaldi, a scientist and artist in the book, says to Klara that "there's growing and widespread concern about AF right now People are afraid what's going on inside",meaning inside the "black box" of AI.

Capaldi then goes on to outline the source of the anxieties that wider society has about the decision-making processes of AI.

These anxieties can be summarised as follows: a lack of transparency, how decisions are made inside the black boxand the rules that are used to make a decision.

For example, if a company is using AI to judge an applicant's creditworthiness, the rules that the algorithm uses to make the decision might give rise to some discomfort.

If an applicant is denied a loan for a small business, one could ask, "Was this a fair decision?"

Similarly, if the government uses AI software in criminal justice and allocating public services like income support, there again is a need for accountability, transparency and public confidence in the process.

There is some good news on this front. There is a proposal going around that all machine-learning research papers should include a section on societal harms, as well as the provenance of their data sets.

Some other areas where regulatory oversight may be needed are: the use of AI by politicians to influence voters and marketing companies who create promotions to persuade people to buy their products.

"Government is already undermined when politicians resort to compelling but dishonest arguments. It could be worse still if victory at the polls is influenced by who has the best algorithm,"an editorial last month in the science journal Nature said.

"With artificial intelligence starting to take part in debates with humans, more oversight is needed to avoid manipulation and harm."

Another idea is that in addition to meeting transparency standards, AI algorithms could be required to undergo trials, akin to those required for new drugs, before they can be approved for public use.

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Artificial intelligence and privacy rights: Daily Star columnist - The Straits Times

Nobel Prize-winning author Kazuo Ishiguro's latest novel Klara and the Sun, his first since receiving the award in literature in 2017, has some relevance for policymakers and ordinary citizens across the globe. The main protagonist in this dystopian science fiction story is Klara, an artificial friend (AF)a human-like teenager who behaves and thinks almost like her cohort of the same age and is a fast learner, as any device or robot using artificial intelligence (AI) can be expected to be. However, what we also learn is that if robots, even if they are super-intelligent, are allowed to make decisions that affect the lives of humans, it might lead to unintended consequences unless there are strict guidelines protecting privacy and other individual rights.

Many discerning readers might already be aware that AI is whipping up quite a storm, particularly as it makes inroads into facial recognition software, law enforcement, and hiring decisions in the corporate world. Policymakers in many countries are alarmed, realising the pros as well as the cons of this revolutionary technology. The European Union (EU), which has been at the forefront of safeguarding privacy rights, unveiled strict regulations in April 2021 to govern the use of AI, a first-of-its-kind policy that outlines how companies and governments can use AI technology, also known as "machine learning". While these new rules are yet to be implemented and are still on the drawing boards, their impact will be similar to that of the General Data Protection Regulation (GDPR), which was drawn up to keep global technology companies such as Amazon, Google, Facebook and Microsoft in check.

The EU has been the world's most aggressive watchdog of the technology industry, with its policies often used as blueprints by other nations. As mentioned, the bloc has already enacted the world's most far-reaching data privacy regulation, the GDPR, and is debating additional antitrust and content-moderation laws. "We have to be aware that GDPR is not made for blockchain, facial or voice recognition, text and data mining [...] artificial intelligence," said Axel Voss, a German member of the European Parliament and one of the creators of GDPR.

AI, in simple terms, is a computer programme that enables the machine to learn how to mimic the problem-solving and decision-making capabilities of the human mind. An AI-enabled device learns how to respond to certain situations and uses algorithms and historical data to recognise a face, predict the weather, or support a search engine like Google. AI is playing a critical role in autonomous vehicles (such as drones and self-driving cars), medical diagnosis, creating art, playing games (such as Chess or Go), search engines, online assistants (such as Siri), image recognition in photographs, spam filtering, predicting flight delays, and much more.

Large technology companies have poured billions of dollars into developing AI, as also have scores of others that use it to develop medicine, underwrite insurance policies, and judge creditworthiness. Governments use versions of AI in criminal justice and in allocating public services like income support. The potential for AI is enormous, and here is the rub. To what extent can machines be manipulated to gain financial or strategic advantage by those who fund this research or manufacture them? Are they really neutral? Can machines adapt to the humans it interacts with, or do existing biases become hard-wired?

Facial recognition algorithms have been at the centre of privacy and ethics debates. Imagine a scenario where a government buys facial recognition software and uses it to track attendees during protest marches. In Hong Kong, the police used a system, created by Cellebrite of Israel, to access the phones of 4,000 protesters. A researcher at an AI global conference claimed to be able to generate facesincluding aspects of a person's age, gender and ethnicitybased on voices.

Digital rights groups across the US, UK and EU have already raised many issues prompted by advances in AI research, and these include privacy violations, ethical concerns, and lack of human control over AI. Ria Kalluri, a machine-learning scientist at Stanford University in California, said, "because training data are drawn from human output, AI systems can end up mimicking and repeating human biases, such as racism and sexism." Kalluri urged her colleagues to dedicate more efforts into tackling scientific questions that make algorithms more transparent and create ways for non-experts to challenge a model's inner workings.

This controversy brings to the fore the role humans play in training the machines. We each have our biases and preferences, and the machines may inherit them. There is increasing support to "debias the algorithms". "Algorithms exclude older workers, trans people, immigrants, children," said Abeba Birhane, a cognitive scientist at the University College Dublin, citing uses of AI in hiring and surveillance. AI had already evoked criticisms from the late Prof Stephen Hawking, one of Britain's pre-eminent scientists, and Elon Musk, the pioneering entrepreneur, has also raised a red flag.

Having said that, it cannot be gainsaid that AI revolution is here and has come to stay. The important question is, how do we harness the power of AI and manage its negative influence? On the positive side, research has conclusively proven that AI is at least as good as humans in determining medical diagnosis. However, rights groups have argued that "people should be told when their medical diagnosis comes from AI and not a human doctor." And using the same logic, one could propose that the same warning be issued if, for example, advertising or political speech is AI-generated.

Coming back to Klara and the Sun, Henry Capaldi, a scientist and artist, says to Klara, "there's growing and widespread concern about AF (artificial friends) right now People are afraid what's going on inside," meaning inside the "black box" of AI. Capaldi then goes on to outline the source of the anxieties that wider society has about the decision-making processes of AI. These anxieties can be summarised as follows: a lack of transparency, how decisions are made inside the black box, and the rules that are used to make a decision. For example, if a company is using AI to judge an applicant's creditworthiness, the rules that the algorithm uses to make the decision might give rise to some discomfort. If an applicant is denied a loan for a small business, one could ask, "Was this a fair decision?" Similarly, if the government uses AI software in criminal justice and allocating public services like income support, there again is a need for accountability, transparency, and public confidence in the process.

There is some good news on this front. There is a proposal going around that all machine-learning research papers should include a section on societal harms, as well as the provenance of their data sets. Some other areas where regulatory oversight may be needed are: the use of AI by politicians to influence voters and marketing companies who create promotions to persuade people to buy their products. "Government is already undermined when politicians resort to compelling but dishonest arguments. It could be worse still if victory at the polls is influenced by who has the best algorithm," according to an editorial in April in the prestigious journal Nature. "With artificial intelligence starting to take part in debates with humans, more oversight is needed to avoid manipulation and harm." Another idea is that in addition to meeting transparency standards, AI algorithms could be required to undergo trials, akin to those required for new drugs, before they can be approved for public use.

Dr Abdullah Shibli is an economist, currently serving as a Senior Research Fellow at the International Sustainable Development Institute (ISDI), a think-tank based in Boston, USA.

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What we need to know about artificial intelligence and privacy rights - The Daily Star

Priya Lakhani, a successful entrepreneur who was giving something back to the developing world, has found a cause much closer to home.

The trained barrister and foodstuffs magnate from London has taken up the cause of artificial intelligence in education, hoping that innovation can help British schools that are falling behind their international counterparts.

For more than a decade, Ms Lakhani used a portion of the profits from her first business, a successful brand of Indian cooking sauces, Masala Masala, to fund schools, as well as vaccinations and hot meals, in India.

But struck by the underachievement rates in schools in the UK, she added a focus on her home country.

I just thought, why am I funding schools in Commonwealth countries that are all replicated on the British model, if the British model just doesn't work? she told The National.

In 2009, the year she first looked to the UK situation, a study conducted by Sheffield University found that a fifth of teenagers in England did not have maths and literacy skills good enough to be able to deal with everyday life challenges.

Three years later results of the 2012 Pisa tests, run by the Organisation for Economic Co-operation and Development, placed the UK in the bottom two thirds in literacy and numeracy on the international rankings table and sparked debate about the needs of the national education system.

In the 12 years since, more money and policies have not made much of a dent in the status quo. Only 65 per cent of primary school pupils in the UK in 2019 achieved the governments expected standard in reading, writing and maths.

Ask any parent, teacher or child and they are likely to support these statistics with personal accounts of a stifling, cumbersome and overloaded system that often fails children.

A leading UK educationalist, Sir Anthony Seldon, has written that teachers in the country are overwhelmed by the administrative demands of classrooms that are too big.

It is an "inherently flawed" model that, he argues, artificial intelligence can help upend. There is no more important issue facing education, or humanity at large, than the fast approaching revolution of AI, he writes in his latest book, The Fourth Education Revolution.

Ms Lakhani is a founder of Century Tech, an AI education technology company developed by a team of teachers, neuroscientists and technologists.

It offers a diagnostics and learning tool that promises to help teach students while reducing teachers' workloads.

The AI-powered system constantly adapts and learns to provide personalised learning experiences to every student. It learns how your brain learns, Ms Lakhani told The National.

Founded in 2013, the platform has been developed by teachers, engineers, data scientists, neuroscientists and psychologists.

Feeling strongly that she needed to "solve the problem", Ms Lakhani went round schools in England and found the same problems Mr Seldon discusses in his book.

The one-size-fits-all delivery of education and the time spent by teachers marking, instead of teaching, was failing the system.

You're asking every teacher to be a data analyst, because they've got to figure out very quickly, which student is where, when you make an intervention. If they didn't do that, in an instant, you go through the curriculum, the gaps widen, Ms Lakhani said.

Teaching methods, she said, evolved from a "blackboard to an interactive whiteboard" without really taking advantage of what technology has to offer.

There was more tech on my phone than in the schools. How is this possible? Has anyone actually looked at this? Ms Lakhani said.

After a crash-course in AI and data-based neuroscience she came up with the idea to build a machine that could host any curriculum in any language and that would track students mouse movements to learn, create predictive patterns and then develop a recommended programme of learning.

Then we could create an artificially intelligent machine that learns by itself and gets smarter every second and can personalise it, thereby removing the one size fits all, she told The National.

Century Tech has one million students using its platform in 40 countries. From Eton in England to the Jumeirah English Speaking School in Dubai and state schools in Lebanon teaching Syrian refugees, a wide range of schools have adopted Century Tech in their schools.

It is not a platform just for fee-paying private schools and Ms Lakhani said about 70 per cent of the schools signed up to her platform in the UK are state schools.

Ms Lakhani said many of Century Techs fastest adopters were in the Middle East, where eight countries use it.

If you want to get some traction and you want to work with some of the brightest and the best, and to innovate with them, then actually the Middle East is a perfect place to be, said Ms Lakhani, who counts MiSK and DAS in Saudi Arabia as clients.

As well as growing her business, quicker and bigger sign-ups also help her platform, and its users, to improve.

Because entrepreneurs that are building innovative products and services want to iterate, they want to be agile, they want to get your feedback, they want to act on it. But if it takes so long to adopt something, then you lose that agility, Ms Lakhani, who was awarded an OBE in 2014, told The National.

An analysis done in conjunction with UCL of students using Century Tech found that on average, students' understanding of a topic increased by 30 per cent between their first and second attempts on the platform. Teachers reported back to Ms Lakhanis team a saving of six to seven hours a week normally spent on admin.

At a cost of 50 to 60 pence per month (70 to 84 US cents) per student, the scalability and wide-reach of their platform looks promising. With worldwide school closures for much of the past year and the shock move to digital distant learning, a glaring spotlight is now shining on the future of education.

More than 600,000 children globally were not achieving the minimum proficiency levels in reading and maths before Covid-19, but with 1.6 billion children out of school at the peak of the pandemic, this number is set to increase.

Adopting AI in education is progressively seen as the way to close the gaps and to boost employment-ready skills.

Just last month Jisc, the UKs not-for-profit organisation providing digital services and solutions in education, launched a new National Centre for Artificial Intelligence in Tertiary Education.

The initiative which has been welcomed by global technology companies including Amazon Web Services, Google and Microsoft aims to deliver AI solutions to 60 colleges and 30 universities within five years.

As well as providing examples of AI in education, including students use of chatbots and digital assistants, a report published by the National Centre pointed to the $3.67bn invested in AI Edtech start-ups in 2019 as a strong economic argument for adoption.

AI education solutions are attracting this investment because they offer considerable benefits to learners, teachers, and education institutions, the report said.

You've got schools that may not have considered using the technology and were forced to because of the pandemic

Ms Lakhani said her company raised 15 million ($21.19m) in funding, the last round of which she said was over-subscribed.

Policymakers have been heralding AI increasing reach into everyday life.

In March this year, the government announced the formulation of an AI Strategy to report to digital secretary, Oliver Dowden. The AI Council, an independent expert government advisory committee of which Ms Lakhani is a member, put forward many of the recommendations. She was recently appointed as a non-executive board member of the governments Digital, Culture, Media and Sport department.

Ms Lakhani thinks the pandemic will accelerate AIs adoption in education. You've got schools that may not have considered using the technology and were forced to because of the pandemic, she said.

Having co-founded the Institute for Ethical AI in Education with Mr Seldon and Prof Rose Luckin, she knows full well the need to put a moral compass on the direction of AI in education.

Ms Lakhani looks to a recent encounter with a schoolgirl for inspiration.

During an observation session of students in England using the Century Tech platform, a schoolgirl told Ms Priya that she used to struggle with mathematics and had always been too afraid to raise her hand in class.

Century Tech, she told her, got her to love maths again and to learn better. It also alerted the teacher when she needed help, making the girls shyness no longer a hindrance.

I just think that's worth 15 million. That girl now feels confident in maths, she feels she can do it. She feels like she gets the help that she needs.

Read more from the original source:

Can artificial intelligence save the British model of education? - The National

Ill admit that I was more than a little skeptical when the Knight Foundation announced last week that it would award $3 million in grants to help local news organizations use artificial intelligence. My first reaction was that dousing the cash with gasoline and tossing a match would be just as effective.

But then I started thinking about how AI has enhanced my own work as a journalist. For instance, just a few years ago I had two unappetizing choices after I recorded an interview: transcribing it myself or sending it out to an actual human being to do the work at considerable expense. Now I use an automated system, based on AI, that does a decent job at a fraction of the cost.

Or consider Google, whose search engine makes use of AI. At one time, Id have to travel to Beacon Hill if I wanted to look up state and local campaign finance records and then pore through them by hand, taking notes or making photocopies as long as the quarters held out. These days I can search for Massachusetts campaign finance reports and have what I need in a few seconds.

Given that local journalism is in crisis, whats not to like about the idea of helping community news organizations develop the tools they need to automate more of what they do?

Well, a few things, in fact.

Foremost among the downsides is the use of AI to produce robot-written news stories. Such a system has been in use at The Washington Post for several years to produce reports about high school football. Input a box score and out comes a story that looks more or less like an actual person wrote it. Some news organizations are doing the same with financial data. It sounds innocuous enough given that much of this work would probably go undone if it couldnt be automated. But lets curb our enthusiasm.

Patrick White, a journalism professor at the University of Quebec in Montreal, sounded this unrealistically hopeful note in a piece for The Conversation about a year ago: Artificial intelligence is not there to replace journalists or eliminate jobs. According to one estimate cited by White, AI would have only a minimal effect on newsroom employment and would reorient editors and journalists towards value-added content: long-form journalism, feature interviews, analysis, data-driven journalism and investigative journalism.

Uh, Professor White, let me introduce you to the two most bottom line-obsessed newspaper publishers in the United States Alden Global Capital and Gannett. If they could, theyd unleash the algorithms to cover everything up to and including city council meetings, mayoral speeches and development proposals. And if they could figure out how to program the robots to write human-interest stories and investigative reports, well, theyd do that too.

Another danger AI poses is that it can track scrolling and clicking patterns to personalize a news report. Over time, for instance, your Boston Globe would look different from mine. Remember the Daily Me, an early experiment in individualized news popularized by MIT Media Lab founder Nicholas Negroponte? That didnt quite come to pass. But its becoming increasingly feasible, and it represents one more step away from a common culture and a common set of facts, potentially adding another layer to the polarization thats tearing us apart.

Personalization of news ... puts the public record at risk, according to a report published in 2017 by Columbias Tow Center for Digital Journalism. When everyone sees a different version of a story, there is no authoritative version to cite. The internet has also made it possible to remove content from the web, which may not be archived anywhere. There is no guarantee that what you see will be what everyone sees or that it will be there in the future.

Of course, AI has also made journalism better and not just for transcribing interviews or Googling public records. As the Tow Center report also points out, AI makes it possible for investigative reporters to sift through thousands of records to find patterns, instances of wrongdoing or trends.

The Knight Foundation, in its press release announcing the grant, held out the promise that AI could reduce costs on the business side of news organizations a crucial goal given how financially strapped most of them are. The $3 million will go to The Associated Press, Columbia University, the NYC Media Lab and the Partnership on AI. Under the terms of the grant, the four organizations will work together on projects such as training local journalists, developing revenue strategies and studying the ethical use of AI. It all sounds eminently worthy.

But there are always unintended consequences. The highly skilled people whom I used to pay to transcribe my interviews no longer have those jobs. High school students who might have gotten an opportunity to write up the exploits of their sports teams for a few bucks have been deprived of a chance at an early connection with news an experience that might have turned them into paying customers or even journalists when they got older.

And local news, much of which is already produced at distant outposts, some of them overseas, is about to become that much more impersonal and removed from the communities they serve.

GBH News contributor Dan Kennedys blog, Media Nation, is online at dankennedy.net.

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Can Artificial Intelligence Help Local News? Sure. And It Can Cause Great Harm As Well. - wgbh.org

HOUSTON, May 18, 2021 (GLOBE NEWSWIRE) -- Onit, Inc., a leading provider of enterprise workflow and artificial intelligence platforms and solutions, including enterprise legal management, contract lifecycle management and business process automation, today announced that it is celebrating its 10th anniversary and a decade of groundbreaking achievements. In 2011, four industry experts started the business with a mission to make it easier for in-house counsel to practice law. Now, it has grown to more than 400 employees and 10,550 customers and has product offerings including workflow automation, business intelligence and artificial intelligence.

The size of the legal technology market is predicted to reach more than $25 billion by 2025. Analysts also forecast that corporate legal departments will spend up to three times more on legal technology in 2025. Onit, with innovative platforms and products for corporate legal, high customer Net Promoter Scores and a focus on innovation, is well-positioned to maintain its market lead.

Onit is 10, and I am extremely proud of what our company leaders, employees and customers have created, commented Eric M. Elfman, Onit CEO and co-founder. Weve progressed significantly from 2011, when we had two employees and $25,000 in revenue, to a company processing more than $5 billion in legal invoices in one year. We started with one no-code platform and a goal to disrupt enterprise legal management and other legacy technologies for corporate legal. Now, weve extended that vision to contract lifecycle management, AI, legal holds, legal service requests and our second AI-powered business intelligence platform, Precedent.

Onit by the NumbersIn honor of its Onitversary, the company has gathered milestones to illustrate its evolution.

About OnitOnit is a global leader of workflow and artificial intelligence platforms and solutions for legal, compliance, sales, IT, HR and finance departments. With Onit, companies can transform best practices into smarter workflows, better processes and operational efficiencies. With a focus on enterprise legal management, matter management, spend management, contract lifecycle management and legal holds, the company operates globally and helps transform the way Fortune 500 companies and billion-dollar corporate legal departments bridge the gap between systems of record and systems of engagement. Onit helps customers find gains in efficiency, reduce costs and automate transactions faster. For more information, visit http://www.onit.com or call 1-800-281-1330.

Media inquiries: Melanie BrennemanOnit(713) 294-7857Melanie.brenneman@onit.com

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Onit Celebrates its 10th Anniversary with Record Revenue Growth, Investment in Artificial Intelligence and Global Expansion - GlobeNewswire

At any given moment, many thousands of new videos are being posted to sites like YouTube, TikTok, and Instagram. An increasing number of those videos are being recorded and streamed live. But tech and media companies still struggle to understand whats going in all that content.

NowMIT alumnus-founded Netra is using artificial intelligence to improve video analysis at scale. The companys system can identify activities, objects, emotions, locations, and more to organize and provide context to videos in new ways.

Companies are using Netras solution to group similar content into highlight reels or news segments, flag nudity and violence, and improve ad placement. In advertising, Netra is helping ensure videos are paired with relevant ads so brands can move away from tracking individual people, which has led to privacy concerns.

The industry as a whole is pivoting toward content-based advertising, or what they call affinity advertising, and away from cookie-based, pixel-based tracking, which was always sort of creepy, Netra co-founder and CTO Shashi Kant SM 06 says.

Netra also believes it is improving the searchability of video content. Once videos are processed by Netras system, users can start a search with a keyword. From there, they can click on results to see similar content and find increasingly specific events.

For instance, Netras system can process a baseball seasons worth of video and help users find all the singles. By clicking on certain plays to see more like it, they can also find all the singles that were almost outs and led the fans to boo angrily.

Video is by far the biggest information resource today, Kant says. It dwarfs text by orders of magnitude in terms of information richness and size, yet no ones even touched it with search. Its the whitest of white space.

Pursuing a vision

Internet pioneer and MIT professor Sir Tim Berners-Lee has long worked to improve machines ability to make sense of data on the internet. Kant researched under Berners-Lee as a graduate student and was inspired by his vision for improving the way information is stored and used by machines.

The holy grail to me is a new paradigm in information retrieval, Kant says. I feel web search is still 1.0. Even Google is 1.0. Thats been the vision of Sir Tim Berners- Lees semantic web initiative and thats what I took from that experience.

At MIT, Kant was also a member of the winning team in the MIT $100K Entrepreneurship Competition (the MIT $50K back then). He helped wri te the computer code for a solution called the Active Joint Brace, which was an electromechanical orthotic device for people with disabilities.

After graduating in 2006, Kant started a company that used AI in its solution called Cognika. AI still had a bad reputation from being overhyped, so Kant would use terms like cognitive computing when pitching his company to investors and customers.

Kant started Netra in 2013 to use AI for video analysis. These days he has to deal with the opposite end of the hype spectrum, with so many startups claiming they use AI in their solution.

Netra tries cutting through the hype with demonstrations of its system. Netra can quickly analyze videos and organize the content based on whats going on in different clips, including scenes where people are doing similar things, expressing similar emotions, using similar products, and more. Netras analysis generates metadata for different scenes, but Kant says Netras system provides much more than keyword tagging.

What we work with are embeddings, Kant explains, referring to how his system classifies content. If theres a scene of someone hitting a home run, theres a certain signature to that, and we generate an embedding for that. An embedding is a sequence of numbers, or vector, that captures the essence of a content. Tags are just human readable representations of that. So, well train a model that detects all the home runs, but underneath the cover theres a neural network, and its creating an embedding of that video, and that differentiates the scene in other ways from an out or a walk.

By defining the relationships between different clips, Netras system allows customers to organize and search their content in new ways. Media companies can determine the most exciting moments of sporting events based on fans emotions. They can also group content by their subjects, locations, or by whether or not they include sensitive or disturbing content.

Those abilities have major implications for online advertising. An advertising company representing a brand like the outdoor apparel company Patagonia could use Netras system to place Patagonias ads next to hiking content. Media companies could offer brands like Nike advertising space around clips of sponsored athletes.

Those capabilities are helping advertisers adhere to new privacy regulations around the world that put restrictions on gathering data on individual people, especially children. Targeting certain groups of people with ads and tracking them across the web has also become controversial.

Kant believes Netras AI engine is a step toward giving consumers more control over their data, an idea long championed by Berners-Lee.

Its not the implementation of my CSAIL work, but Id say the conceptual ideas I was pursuing at CSAIL come through in Netras solution, Kant says.

Transforming the way information is stored

Netra currently counts some of the countrys largest media and advertising companies as customers. Kant believes Netras system could one day help anyone search through and organize the growing ocean of video content on the internet. To that end, he sees Netras solution continuing to evolve.

Search hasnt changed much since it was invented for web 1.0, Kant says. Right now theres lots of link-based search. Links are obsolete in my view. You dont want to visit different documents. You want information from those documents aggregated into something contextual and customizable, including just the information you need.

Kant believes such contextualization would greatly improve the way information is organized and shared on the internet.

Its about relying less and less on keywords and more and more on examples, Kant explains. For instance, in this video, if Shashi makes a statement, is that because hes a crackpot or is there more to it? Imagine a system that could say, This other scientist said something similar to validate that statement and this scientist responded similarly to that question. To me, those types of things are the future of information retrieval, and thats my lifes passion. Thats why I came to MIT. Thats why Ive spent one and a half decades of my life fighting this battle of AI, and thats what Ill continue to do.

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Improving the way videos are organized - MIT News

Artefact and Econocom have teamed up to raise Service Desk quality standards using artificial intelligence

Paris, May 20, 2021 7:30 am CEST - Artefact (FR0000079683 ALATF eligible for PEA-PME equity savings plans), an expert in datatransformation and digital marketing for major brands, has joined forces with Econocom, a digital workplace specialist and Frances number one user environment facilities manager1, to power the user support provided by its Service Desks with artificial intelligence.

Their partnership has produced a dedicated artificial intelligence-based solution for Service Desks delivering high-quality service and responses for todays increasingly demanding and digital-reliant users, giving them a fluid, ultra-responsive and effective experience.

In 2018, even before homeworking snowballed as a result of the Covid-19 pandemic, Service Desks saw requests leap 61% higher2. Despite this surge in demand, keeping costs down remains a top priority, especially during times of crisis.

Artificial intelligence puts clear information and robust response protocols at the fingertips of Econocoms teams, especially in service centers, and that helps to deliver enhanced user satisfaction. In this approach, more simple queries are handled in an automated fashion by means of voice assistants based on Natural Language Processing (NLP) algorithms, while more complex queries are passed on to operators assisted by artificial intelligence systems. Higher value-added support is thus provided by the augmented human channel.

Harnessing the tight fit between their areas of expertise, Econocom and Artefact have pioneered in France this next generation of solutions yielding three key benefits: greater user satisfaction, improved quality of service and lower operating costs.

There are four principal applications of artificial intelligence in Service Desks, and these are all found in the solutions provided by Econocom with technical support from Artefact:

The extraordinary wealth of operating data coupled with the latest machine learning and analysis capabilities of artificial intelligence can give support units an unprecedented ability to understand and resolve the incidents encountered by users, commented Vincent Luciani, co-CEO and co-founder of Artefact.

Artificial intelligence can be adopted more easily because no one needs to change how they work. Whether the Service Desk is contacted by phone, by email or by chat, artificial intelligence can help improve users experience by complementing the channel predicated on existing tools (ITSM, knowledge base, UEM, etc.).

Lastly, the fact that the data gathered by the Service Desk relates to users should not be overlooked. Some of it may be of a personal nature, which means it is covered by the GDPR framework. Accordingly, before applying any artificial intelligence, Econocom and Artefact have taken steps to verify and control the conformity of data collection, the location where the data is processed and whether any metadata is retained.

The use of artificial intelligence should not eclipse the fundamentals of running a Service Desk, which require a fluid experience and rapid response on the user side, and clear, relevant information available on the agent side. We have implemented these solutions on several occasions, including for Econocoms 6,000 employees in France and for a major player in the energy sector. The feedback we have received has been extremely positive. So, in our view, artificial intelligence is revolutionizing the quality of responses provided, which helps to enhance end user satisfaction and to augment productivity end-to-end, concluded Long Le Xuan, Chief Executive Officer, Econocom Infogrance Systmes.

About Artefact Iartefact.com

Artefact is a next-generation data-driven consulting and services firm, transforming data into value and business impact for its clients. With a strong presence on the world's main markets (France, Germany, the UK, Asia, Dubai, the USA), Artefact serves an extensive portfolio of more than 300 clients, including a host of world leaders such as Samsung, Danone, LOral and Sanofi. The Group has three main service offerings, leveraging its data mining and data analysis capacities: Data Consulting, Data Marketing and Digital Activation. Artefact is listed on the Euronext growth stock exchange in Paris (ISIN code: FR0000079683).

Artefacts press contacts: Delphine Bionne | + 33 6 74 74 11 48 | delphine@thebraincontent.fr

About ECONOCOM Ieconocom.com

As a digital general contractor, Econocom conceives, finances, and facilitates the digital transformation of large firms and public organisations. With 45 years experience, it is the only market player offering versatile expertise through a combination of project financing, equipment distribution and digital services. The group is present in 18 countries, with over 9,000 employees and 2,559 m in revenue. Econocom is listed on Euronext in Brussels, on the BEL Mid and Family Business indices.

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1 Teknowlogy/Pac rankings, 20022 HDI Practices & Salary Report, 2018

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Artefact and Econocom have teamed up to raise Service Desk - GlobeNewswire

Can an artificial intelligence system automate text analysis without losing accuracy? Jaguar Land Rover has integrated AI into its brand tracking with the aim of doing just that. By Liam Kay

Humans are experts at deciphering language and picking up the nuances in speech, and themeaning behind words. In the ever demanding world of brand tracking, can rigour and analysis be retained in research while increasing speed and reducing cost?

A partnership between research agency MM Eye and Jaguar Land Rover aimed to explore whether an artificial intelligence (AI) system could help uncover customer sentiment in answers to open-ended questions and save the brand time and money.

The project started in 2017 and was intended to help Jaguars brand health survey, examining customer views and feelings towards the company. Specifically, its goal was to explore whether long-form, verbatim responses to questions posed by thesurvey could be analysed by AI instead of humancoders without losing the necessary level of detail and quality.

MM Eye has worked with Jaguar since 2010, and was involved with its brand health survey before the AIproject. The survey has typically used stream-of-consciousness interviewing techniques to analyse consumers ...

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Driving in automatic: How Jaguar Land Rover used artificial intelligence to track sentiment | Feature - Research Live

Artificial intelligence (AI) is everywhere: personal digital assistants answer our questions, robo-advisors trade stocks for us, and driverless cars will someday take us where we want to go. AI has penetrated our lives, and its use is exploding in biomedical research and health careincluding across all dimensions of cancer research, where the potential applications for AI are vast.

Artificial Intelligence (AI)is a computer performing tasks commonly associated with human intelligence. Humans are coding or programing a computer to act, reason, and learn. Analgorithm or modelis the code that tells the computer how to act, reason, and learn.

Machine Learning (ML)is a type of AI that is not explicitly programmed to perform a specific task but rather can learn iteratively to make predictions or decisions. The more data an ML model is exposed to, the better it performs over time.

Deep Learning (DL)is a subset of ML that uses artificial neural networks modeled after how the human brain processes information to learn from huge amounts of data. A well-designed and well-trained DL model is able to perform classification tasks and make predictions with high accuracy, sometimes exceeding human expertlevel performance.

AI excels at recognizing patterns in large volumes of data, extracting relationships between complex features in the data, and identifying characteristics in data (including images) that cannot be perceived by the human brain. It has already produced results in radiology, where clinicians use computers to process images rapidly, thus allowing radiologists to focus their time on aspects for which their technical judgment is critical. For example, last year, the Food and Drug Administration approved the first AI-based software to process images rapidly and assist radiologists in detecting breast cancer in screening mammograms.

Integration of AI technology in cancer care could improve the accuracy and speed of diagnosis, aid clinical decision-making, and lead to better health outcomes. AI-guided clinical care has the potential to play an important role in reducing health disparities, particularly in low-resource settings. NCI will invest in supporting research, developing infrastructure, and training the workforce to help achieve these goals and more.

NCI-funded research has already led to several opportunities for the use of AI.

Scientists inNCIs intramural research programare leveraging the capabilities of AI to improve cancer screening in cervical and prostate cancer. NCI investigators developed a deep learning approach for the automated detection of precancerous cervical lesions from digital images. Read more about this inMark's story.

Another group of NCI intramural investigators and their collaborators trained a computer algorithm to analyze MRI images of the prostate. Historically, standard biopsies of the prostate did not always produce the most accurate information. Starting 15 years ago, clinicians at NCI began performing biopsies guided by findings from MRI, enabling them to focus on regions of the prostate most likely to be cancerous. MRI-guided biopsy improved diagnosis and treatment when utilized by prostate cancer experts, but the method did not transfer well to clinics without prostate cancer expertise. The NCI clinicians used AI to capture their diagnostic expertise and made the algorithm accessible to clinics across the country as a tool to help with diagnosis and clinical decision-making.

The full potential of the MRI-guided biopsy developed by NCI researchers is being realized in clinics without prostate cancerspecific expertise because of this AI tool. New AI algorithms under development now aim to surpass the capabilities of well-trained radiologists by enabling the prediction of patient outcomes from MRI.

AI methods can also be used to identify specific gene mutations from tumor pathology images instead of using traditional genomic sequencing. For instance, NCI-funded researchers at New York University used deep learning(DL) to analyze pathology images of lung tumorsobtained fromThe Cancer Genome Atlas. Not only could the DL method accurately distinguish between two of the most common lung cancer subtypes, adenocarcinoma and squamous cell carcinoma, it could predict commonly mutated genes from the images.

In the context of brain tumors, identifying mutations using noninvasive techniques is a particularly challenging problem. With NCI support, an international team, including investigators at Harvard University and the University of Pennsylvania, recently developed a DL method to identifyIDHmutations noninvasively from MRI images of gliomas. These research findings suggest that, in the future, AI could help identify gene mutations in innovative ways.

NCI is leveraging the power of AI in multiple ways to discover new treatments for cancer. TheCancer Moonshotis supporting two major efforts in partnership with the Department of Energy (DOE) to leverage its supercomputing expertise and power for cancer research. In one effort,AI is being used to detect and interpret features of target molecules(e.g., proteins or nucleic acids that are important in cancer growth), make predictions for new drugs to target those molecules, and help evaluate the effectiveness of those drugs. Research is also being done to identify novel approaches for creating new drugs more effectively.

A project that is part of the second effort is usingcomputational methods to model the interaction of KRAS protein with the cell membranein detailed ways that were not previously possible. A cross-agency research team collaborating with theRAS Initiativedeveloped a model of KRASlipid membrane binding to simulate the behavior of KRAS at the membrane. This model could help identify novel ways to inhibit the activity of mutant KRAS protein. This work will help scientists find new avenues to target mutations in theKRASgene, one of the most frequently mutated oncogenes in tumors. In the future, this could be applied to other important oncogenes.

The NCIDOE collaboration is also enabling the application of DL to analyze patient information and cancer statistics collected by theNCI Surveillance, Epidemiology, and End Results (SEER) program. As part of this effort, DL algorithms were developed to extract tumor features automatically from pathology reports, saving thousands of hours of manual processing time. The goal of the project is to transform cancer care by applying AI capabilities to population-based cancer data in real time. This will help us better understand how new diagnostic methods, treatments, and other factors affect patient outcomes. Real-time data analysis will also allow for newly diagnosed individuals to be linked with clinical trials that may benefit them. NCIs long-term investment in the SEER program and its infrastructure, coupled with newer investments in AI, will enable pattern recognition in population data that was impossible before. AI will aid in predicting treatment response, likelihood of recurrence (local or metastatic), and survival.

The potential applications of AI in medicine and cancer research hold great promise. Leveraging these opportunities will require increasing investments and addressing some challenges that will have to be overcome.

The data science and AI communities will be important partners in realizing the promise of AI in cancer research. NCI can engage these communities by providing appropriate funding opportunities and access to data sources; linking cancer researchers and AI researchers; and supporting the training and development of a workforce with expertise in AI, data science, and cancer. Building on the NCIDOE collaboration, a series of workshops are being held to build a community engaged in pushing the limits of current computational practices in cancer research to develop new computational technologies.

Currently, the use of AI in cancer research and care is in its infancy. Most research is focused on methods development, rather than on implementing those methods in clinical practice. NCI has an opportunity to lead the way in implementing AI in cancer care by supporting research to find effective pathways for clinical integration (including ways to understand uncertainty and validate AI approaches), educating medical personnel about the strengths and weaknesses of the technology, and rigorously assessing its benefits in terms of clinical outcomes, patient experience, and costs.

The lack of large, publicly available, well-annotated cancer datasets has been a significant barrier for AI research and algorithm development. The lack of benchmarking datasets in cancer research hampers reproducibility and validation. Support for annotation, harmonization, and sharing of standardized cancer datasets to drive AI innovation and support training and validation of AI models will be essential. With even greater volumes of data anticipated in the future, support for developing approaches to generate and aggregate new research and clinical data coherently will be critical for long-term success.

To support this work and to make cancer data broadly available for all types of research, NCI is refining policies and practices to enhance and improve data sharing. As part of those efforts, NCI is building aCancer Research Data Commons (CRDC). One node of the CRDC is an Imaging Data Commons that will connect toThe Cancer Imaging Archive, a unique resource of publicly available, archival cancer images with supporting data to enable discovery. NCI also recently launched theChildhood Cancer Data Initiativeto accelerate progress for children, adolescents, and young adults with cancer by optimizing the collection, aggregation, and utility of research and clinical data.

NCIs data aggregation and sharing efforts are crucial to moving AI and many areas of cancer research forward. As new sources of biomedical and health data emerge, the amount of information will continue growing faster than it can be interrogated. AI will be an essential tool for processing, aggregating, and analyzing the vast amounts of information the data hold to drive discovery and improve patient care.

One challenge of AI, and DL specifically, is the black box problem: not fully understanding what features of the data a computer has used in its decision-making process. For example, a DL algorithm that predicts the optimal treatment for a patient does not provide the reasoning it used to make that prediction. Additional efforts are needed to reveal how algorithms arrive at a decision or prediction so that the process becomes transparent to scientists and clinicians. Making these algorithms transparent could help researchers identify new biological features relevant to disease diagnosis or treatment.

Incorporating information about biological processes into the algorithm is likely to improve its accuracy and decrease dependence on large amounts of annotated data, which may not be available. One danger of the black box problem is that DL may inadvertently perpetuate existing unconscious biases. Researchers need to carefully consider how potential biases affect the data being used to develop a model, adopt practices to address and monitor those biases, and monitor performance and applicability of AI models.

With increased investments, NCIs efforts to realize AIs potential will lead to more accurate and rapid diagnoses, improved clinical decision-making, and, ultimately, better health outcomes for patients with cancer and those at risk.

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Artificial Intelligence - National Cancer Institute

In this blog on What is Artificial Intelligence, were going to talk about what Artificial Intelligence is and how it is useful for us. Lets begin by looking at the following concepts:

Do you think the concept and existence of Artificial Intelligence is new?

Well, when there was no internet in the past people were researching on Artificial Intelligence by reading books or checking out articles in the newspaper.

What is Artificial Intelligence? Well, this key term sure did start a lot of curiosity back in the day!

People wanted to knowif they could teach computers to learn like how a young child does. The concept here was to basically use trial and error to develop formal reasoning.

The term Artificial Intelligence was actually coined way back in 1956 by John McCarthy, a professor at Dartmouth.

For years, it was thought that computers would never match the power of the human brain, but this has proven to not be the case.

Well, back then we did not have enough data and computation power, but now with Big Data coming into existence and with the advent of GPUs, Artificial Intelligence is possible.

Did you know that 90% of the worlds data has been generated in the past two years alone? Computers can make sense of all this information more quickly.

Very soon, we can see Artificial Intelligence being a little less artificial and a lot more intelligent.

Artificial Intelligence in my opinion, is the simulation of human intelligence done by machines programmed by us. The machines need to learn how to reason and do some self-correction as needed along the way.

Now that we have detailed algorithms which Artificial Intelligence systems can make use of, they can perform huge tasks faster and more efficiently.

Machine Learning and Deep Learning are just ways to achieve Artificial Intelligence.

In contrast, some AI experts believe such projections are wildly optimistic given our limited understanding of the human brain and believe that Artificial Intelligence is still centuries away.

In this blog about What is Artificial Intelligence lets now walk-through some of the types of Artificial Intelligence.

Artificial Intelligence can be popularly categorized in two ways:

Lets talk about Narrow AI:

Narrow AI is an Artificial Intelligence System that is designed and trained for one particular task. Virtual assistantssuch as Amazons Alexa and Apples Siri use narrow AI.

Narrow AI is sometimes also referred to as Weak AI.However, that doesnt mean that Narrow AI is inefficient or something of that sort.

On the contrary, it is extremely good at routine jobs, both physical and cognitive. It is Narrow AI that is threatening to replace many human jobs throughout the world.

However, my curiosity on What is Artificial Intelligence didnt stop here. I was digging a little bit further.

Heres when I found out more aboutWide AI:

Wide AI is a system with cognitive abilities so that when the system is presented with an unfamiliar task, it is intelligent enough to find a solution.

Here the system is capable of having intelligent behavior across a variety of tasks from driving a car to telling a joke.

The techniques aim at replicating and surpassing many (ideally all) capacities of human intelligence such as risk analysis and other cognitive processes.

Artificial Intelligence is used almost everywhere today, in systems such asMail spam filtering, Credit-Card fraud detection systems, Virtual Assistance and so on.

I believe there is no end or limitation to the number of applications we have with Artificial Intelligence to make our lives better!

Next up, in this What is Artificial Intelligence blog, lets go through some of the use cases that I believe stand out.

Well, in the late 90s when the common man was still wondering what is Artificial Intelligence? We had computers trained to play games and solve basic problems.

Deep Blue was a chess-playing computer developed by IBM.

It is known for being the first computer chess-playing system to win both a chess game and a chess match against a reigning world champion under regular time controls.

Today, the Artificial Intelligence available on the free chess games on your phones are exponentially faster and better than Deep Blue.

What we majorly require is the use of Artificial Intelligence and technology to ensure that help arrives faster. We can start by developing systems which help first responders find victims of earthquakes, floods, and any other natural disasters.

Normally, responders need to examine aerial footage to determine where people could be stranded. However, examining a vast number of photos and drone footage is very time and labor intensive.

This is a time critical process and it might very well be the difference between life and death for the victims.

An Artificial Intelligence system developed at Texas A&M University permits computer programmers to write basic algorithms that can examine extensive footage and find missing people in under two hours.

Hunting of Wildlife species and poaching is a global problem as it leads to extinction.

For example, the latest African census showed a 30% decline in elephant populations between 2007 and 2014. Wildlife conservation areas have been established to protect these species from poachers, and these areas are protected by park rangers. The Rangers, however, do not always have the resources to patrol the vast areas efficiently.

Ugandas Queen Elizabeth National Park uses Predictive modeling to predict poaching threat levels. Such models can be used to generate efficient and feasible patrol routes for the park rangers.

In my opinion, Neural networks work well to provide smart agricultural solutions.

Everything ranging from complete monitoring of the soil and crop yield to providing predictive analytic models to track and predict various factors and variables that could affect future yields.

For example, the Berlin-based agricultural tech startup PEAT has developed a deep learning algorithm-based application called Plantix which can identify defects and nutrient deficiencies in the soil.

Their algorithms correlate particular foliage patterns with certain soil defects, plant pests and diseases.

Well, one day youre wondering What is Artificial Intelligence and later robots are ready to perform surgical procedures on you?

Robots today are machine learning-enabled tools that provide doctors with extended precision and control. These machines enable shortening the patients hospital stay, positively affecting the surgical experience and reducing medical costs all at once.

Similarly, mind-controlled robotic arms and brain chip implants have begun helping paralyzed patients regain mobility and sensations of touch.

Overall, Machine learning and Artificial Intelligence are helping improve patient experience on the whole.

It is amazing to see that applications like iNaturalist and eBirds collect data on the species encountered.This helps keep track of species populations, ecosystems and migration patterns.

As a result, these applications also have an important role in the better identification and protection of marine and freshwater ecosystems as well.

Do check out this link for more information on the blog about Artificial Intelligence with Deep Learning!

I personally believe that Artificial Intelligence will revolutionize all aspects of our daily life. It will be subtle enough and have a big impact on everything around us!

I hope you have enjoyed my post on what is Artificial Intelligence. If you have any questions, mention it in the comments section, I will reply ASAP.

This video on Artificial Intelligence gives you a brief introduction to AI and how AI can change the world.

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What is Artificial Intelligence | Artificial Intelligence ...