Tag Archives: ITU

Updates from ITU

Meet your virtual avatar: the future of personalized healthcare
ITU News – Tingly? Sharp? Electric? Dull? Pulsing?

Trying to describe a pain you feel to your doctor can be a difficult task. But soon, you won’t have to: a computer avatar is expected to tell your doctor everything they need to know.

The CompBioMed Centre of Excellence, an international consortium of universities and industries, is developing a program that creates a hyper-personalized avatar or ‘virtual human’ using a supercomputer-generated simulation of an individual’s physical and biomedical information for clinical diagnostics.

There is a rapid and growing need for this kind of technology-enabled healthcare. 12 million people who seek outpatient medical care in the U.S. experience some form of diagnostic error. Additionally, the World Health Organization estimates that there will be a global shortage of 12.9 million healthcare workers by 2035.

Greater access to technology-enabled healthcare will allow doctors to make better and faster diagnoses – and provide the tools to collect the necessary data.

The Virtual Human project combines different kinds of patient data that are routinely generated as part of the current healthcare system, such as x-rays, CAT scans or MRIs to create a personalized virtual avatar. more>

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Updates from ITU

New ITU standard to introduce Machine Learning into 5G networks
ITU News – A new ITU standard has established a basis for the cost-effective integration of Machine Learning into 5G and future networks.

The standard – ITU Y.3172 –  describes an architectural framework for networks to accommodate current as well as future use cases of Machine Learning.

“Machine Learning will change the way we operate and optimize networks,” says Slawomir Stanczak, Chairman of the ITU-T Focus Group on ‘Machine Learning for Future Networks including 5G’.

“Every company in the networking business is investigating the introduction of Machine Learning, with a view to optimizing network operations, increasing energy efficiency and curtailing the costs of operating a network,” says Stanczak. “This ITU Y.3172 architectural framework provides a common point of reference to improve industry’s orientation when it comes to the introduction of Machine Learning into mobile networks.”

Machine Learning holds great promise to enhance network management and orchestration.

Drawing insight from network-generated data, Machine Learning can yield predictions to support the optimization of network operations and maintenance. more>

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Updates from ITU

Dark skies, bright future: overcoming Nigeria’s e-waste epidemic
By Eloise Touni – Nigerian law prohibits burning plastic cables, as well as acid leaching and other common methods used by John and his fellow pickers to reclaim valuable metals from discarded electronics. But minimal enforcement and a low awareness of the risks they are running means most pickers continue to regularly expose themselves to toxins that cause respiratory and dermatological problems, eye infections, neurodevelopmental issues, and, ultimately, shorter lives.

While international agreements like the Basel Convention prohibit the import of hazardous waste, unscrupulous importers and a porous customs system mean Nigeria now ranks alongside Ghana as one of the world’s leading destinations for electronic waste. The country receives 71,000 tonnes of used consumer goods through the two main ports in Lagos from the European Union and other more industrialized economies every year.

“Some of the e-waste from abroad is comprised of cathode-ray TVs, which contain lead, as well as refrigerators and air conditioners containing hydrochlorofluorocarbons, making it a threat to those who are dismantling and dealing with the products,” the UN Environment Program’s Eloise Touni says.

Plastic components, including hard casings and cables, also contain persistent organic pollutants used as flame retardants, such as polybrominated diphenyl ethers (PBDE).

These were banned by the Stockholm Convention due to their long-lasting global impacts and are regularly detected in ecosystems and people all over the world, including in Arctic wildernesses and their traditional inhabitants. more>

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Updates from ITU

If we want to solve climate change, water governance is our blueprint
By Elizabeth Taylor – The phrase “fail to prepare or prepare to fail” comes to mind as we enter an era in which governments and communities must band together to mitigate climate change. Part of what makes our next steps so uncertain is knowing we must work together in ways that we have – so far – failed to do. We either stall, or offer up “too little, too late” strategies.

These strategies include cap-and-trade economic incentive programs, like the Kyoto Protocol and other international treaties. Insightful leaders have drawn attention to the issue, but lukewarm political will means that they are only able to defer greenhouse gas emissions-reduction targets in the future. A global crisis demands global commitment. How can we work together to face a universal threat? What of the complex challenges that demand unified monitoring and responses?

One principal impediment is the lack of coherent technical infrastructure.

Currently, our arsenal for facilitating collective action is understocked. Our policies are unable to invoke tide-turning change because they lack a cohesive infrastructure. In the absence of satisfactory tools to make them happen, our policies and pledges become feelgood initiatives rather than reaching full effectiveness.

What tools might lead us to act collectively against climate change? It’s easy to focus on the enormous scale of global cooperation needed, or the up-front investments it will take to mitigate the crisis. But as the writer E.L. Doctorow reminded us, we can’t be intimidated by the process: “Writing a novel is like driving a car at night,” he said. “You can see only as far as your headlights, but you can make the whole trip that way.”

We don’t have to possess all the answers as we set out to save our communities. We don’t have to know exactly what we will meet along the way. At a minimum, we must only understand how to use our headlights to see the first few feet ahead of us.

So what is the first step on our path?

It is the substance that underpins our industry, health and survival. It remains a central source of conflict around the world, yet it also creates partnerships. Our first step is water.

Water challenges us with issues of scarcity, quality and distribution. It may seem to be a local issue, but combined with local tensions and a globalized economy, water governance is set to become one of our greatest tests of diplomatic finesse and technological synergy.

If we can properly align local and global water governance and management, we can prepare the tools, the organizational blueprint and the political momentum needed to solve climate change. more>

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Updates from ITU

Iceland’s data centers are booming—here’s why that’s a problem
By Tryggvi Adalbjornsson – The southwestern tip of Iceland is a barren volcanic peninsula called Reykjanesskagi. It’s home to the twin towns of Keflavik and Njardvik, around 19,000 people, and the country’s main airport.

On the edge of the settlement is a complex of metal-clad buildings belonging to the IT company Advania, each structure roughly the size of an Olympic-size swimming pool. Less than three years ago there were three of them. By April 2018, there were eight. Today there are 10, and the foundations have been laid for an 11th.

This is part of a boom fostered partly by something that Icelanders don’t usually rave about: the weather.

Life on the North Atlantic island tends to be chilly, foggy, and windy, though hard frosts are not common. The annual average temperature in the capital, Reykjavík, is around 41 °F (5 °C), and even when the summer warmth kicks in, the mercury rarely rises above 68. Iceland has realized that even though this climate may not be great for sunning yourself on the beach, it is very favorable to one particular industry: data.

Each one of those Advania buildings in Reykjanesskagi is a large data center, home to thousands of computers. They are constantly crunching away, processing instructions, transmitting data, and mining Bitcoin. Data centers like these generate large amounts of heat and need round-the-clock cooling, which would usually require considerable energy. In Iceland, however, data centers don’t need to constantly run high-powered cooling systems for heat moderation: instead, they can just let in the brisk subarctic air.

Natural cooling like this lowers ongoing costs. more>

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Updates from ITU

AI, quantum technologies and new cyber threats – are we prepared?
ITU News – Quantum computing is on the horizon. The emerging computing architecture renders possible a form of ‘super parallel processing’ based on quantum physics that can rapidly solve problems beyond the scope of what a classical computer can achieve.

Quantum computing is fast advancing, with governments investing billions and blue-chip technology heavyweights prioritizing the technology.

With far-reaching implications for data security, advances in quantum computing risk unraveling data encryption, with far-reaching implications for security.

What this means is that quantum computers will be incredibly effective at hacking into encrypted data – rendering sensitive data and critical infrastructures, as well as Internet of Things and 5G networks, vulnerable to attack.

Although the technology is not yet commercially deployed, the security threats are already here.

The ‘download now, decrypt later’ attack vector already sees actors downloading existing encrypted data, to be cracked open once the technology arrives.

“Now, it’s not a matter of if it will happen,” said Mark Jackson of Cambridge Quantum Computing during a panel discussion on AI, quantum technologies and new cyber threats at the recent AI for Good Global Summit. more>

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Updates from ITU

How AI can improve agriculture for better food security
ITU News – Roughly half of the 821 million people considered hungry by the United Nations are those who dedicate their lives to producing food for others: farmers.

This is largely attributed to the vulnerability of farmers to agricultural risks, such as extreme weather, conflict, and market shocks.

Smallholder farmers, who produce some 60-70% of the world’s food, are particularly vulnerable to risks and food insecurity.

Emerging technologies such as Artificial Intelligence (AI), however, have been particularly promising in tackling challenges such as lack of expertise, climate change, resource optimization and consumer trust.

AI assistance can, for instance, enable smallholder farmers in Africa to more effectively address scourges such as viruses and the fall armyworm that have plagued the region over the last 40 years despite extensive investment, said David Hughes, Co-Founder of PlantVillage and Assistant Professor at Penn State University at a session on AI for Agriculture at last week’s AI for Good Global Summit. more>

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Updates from ITU

AI for Good’ or scary AI?
By Neil Sahota and Michael Ashley – Some futurists fear Artificial Intelligence (AI), perhaps understandably. After all, AI appears in all kinds of menacing ways in popular culture, from the Terminator movie dynasty to homicidal HAL from 2001: A Space Odyssey.

Though these movies depict Artificial General Intelligence (AGI) gone awry, it’s important to note some leading tech scholars, such as George Gilder (author Life After Google), doubt humans will ever be able to generate the sentience we humans take for granted (AGI) in our machines.

As it turns out, the predominant fear the typical person actually holds about AI pertains to Artificial Narrow Intelligence (ANI).

Specialized, ANI focuses on narrow tasks, like routing you to your destination — or maybe one day driving you there.

Much of what we uncovered when cowriting our new book, Own the A.I. Revolution: Unlock Your Artificial Intelligence Strategy to Disrupt Your Competition, is that people fear narrow task-completing AIs will take their job.

“It’s no secret many people worry about this type of problem,” Irakli Beridze, who is a speaker at the upcoming AI For Good Global Summit and heads the Centre for Artificial Intelligence and Robotics at the United Nations Interregional Crime and Justice Research Institute, told us when interviewed for the book.

“One way or another, AI-induced unemployment is a risk we cannot dismiss out of hand. We regularly see reports predicting AI will wipe out 20 to 70 percent of jobs. And we’re not just talking about truck drivers and factory workers, but also accountants, lawyers, doctors, and other highly skilled professionals.” more>

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Updates from ITU

What do ‘AI for Social Good’ projects need? Here are 7 key components.
By Anna Bethke – At their core, ‘AI for Social Good’ projects use artificial intelligence (AI) hardware and software technologies to positively impact the well-being of people, animals or the planet – and they span most, if not all, of the United Nations Sustainable Development Goals (SDGs).

The range of potential projects continues to grow as the AI community advances our technology capability and better understands the problems being faced.

Our team of AI researchers at Intel achieved success by working with partners to understand the problems, collecting the appropriate data, retraining algorithms, and molding them into a practical solution.

At their core, an AI for Social Good project requires the following elements:

  1. A problem to solve, such as improving water quality, tracking endangered species, or diagnosing tumors.
  2. Partners to work together in defining the most complete view of the challenges and possible solutions.
  3. Data with features that represent the problem, accurately labeled, with privacy maintained.
  4. Compute power that scales for both training and inference, no matter the size and type of data, or where it lives. An example of hardware choice is at ai.intel.com/hardware.
  5. Algorithm development, which is the fun part! There are many ways to solve a problem, from a simple logistic regression algorithm to complex neural networks. Algorithms match the problem, type of data, implementation method, and more.
  6. Testing to ensure the system works in every way we think it should, like driving a car in rain, snow, or sleet over a variety of paved and unpaved surfaces. We want to test for every scenario to prevent unanticipated failures.
  7. Real-world deployment, which is a critical and complicated step that should be considered right from the start. Tested solutions need a scalable implementation system in the real world, or risk its benefits not seeing the light of day.

At the end of May, Intel AI travels to Geneva, Switzerland, for the UN’s AI for Good Global Summit hosted by ITU and will speak to each of these elements in a hands-on workshop. more>

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Updates from ITU

25 ways to be a more inclusive engineer

This list, created in support of EQUALS by members of its Leadership Coalition, highlights 25 actions that individual engineers can take to be more inclusive, as a complement to steps taken by employers.

Business Leadership

  1. Be sensitive to the impact of micro-inequities. Pay attention to language and assumptions in daily conversations that may inadvertently reinforce stereotypes.
    Listen for and correct personality penalties in casual conversation.
    Interrupt “fixed mindsets” talk by questioning language such as “natural talent,” “born leaders,” “not leadership material,” “a leopard doesn’t change its spots,” or “either you’ve got that special something or you don’t.”
  2. Encourage others to apply or ask for a certain position, award or role.
    Never underestimate the power of simply encouraging others to take on a project or apply for a position you think they are qualified to do,[iv] but do so in ways that do not set people up to fail.

  3. Ensure that the ideas, solutions and approaches of women and men team members are given equal consideration and are not discounted because of gender.
    Ensure that credit goes to the originator of a good point and not just to whoever talked the longest or the loudest, or to the person who repeated someone else’s idea.

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