Category Archives: Ai

It takes a couple of people and the process takes about two hours, Singer explained.

Robust is based in San Francisco and launched in 2019, founded by Harvard University academics Kojin Oshiba and Singer (pictured, left to right). The San Francisco-based insurtech describes itself as an AI security and reliability start-up. Translated, its AI-base helps sift out errors in other AI technology or related data models before they go into production and are used.

Customers come from industry areas including insurance, medical devices, cruise companies, genome analysis firms, travel industry outfits and neobanks (banks that operate online without traditional physical branch networks).

Approximately 50 people work for Robust, which has raised more than $45 million in venture capital financing to date.

Robust announced in May that it would partner with Tokio Marine with the idea of protecting its AI systems from unintentional data input/contamination that can lead to unexpected and incorrect results.

Masashi Namatame, Tokio Marine Groups chief digital officer, noted in prepared remarks that the company uses AI in various business areas including claims services, product recommendations and customer support. He said the company is committed to working with Robust to manage corresponding AI risks and vulnerabilities that are otherwise very hard to recognize.

Together, the companies are also focusing on jointly researching and developing products to boost AI security. The deal calls for a broader look at AI security in the Japanese market and the global insurance industry.

In terms of the initial software partnership, Singer said there were several months of discussions before an agreement. Then came the signing of a production license with Tokio Marine before a few Robust engineers installing the software, starting with Tokio Marine dR.

Engineers completed the initial process within about two hours, leaving the option to expand usage into other departments over time.

One can basically create more and more instances of that software within the company to serve more teams, Singer explained.

In the meanwhile, Robust offers customer support as needed.

Its completely out of the box, Singer said.

AI technology partnerships with big companies can often be more complex and time-consuming, Singer acknowledged.

A lot of times integration costs are very high there is a lot of back and forth, a lot of manual installation, Singer said. The product weve created is almost like zero integration. Its very easy to use and automates a lot of the processes that data scientists normally do and use.

As Singer explains it, organizations using AI technology typically have data scientists on staff, with some focused on debugging models - checking models before they go into production and regularly assessing whether edits are needed. Robusts software does that automatically.

One of the biggest selling points for any company is the fact that were able to reduce that overhead from the data science teams, Singer said. Were able to have the data science teams become 25% to 40% more effective because they no longer have to do these things.

Employee take-up of the software is immediate, Singer said.

Software for offices and personal devices alike needs period technology updates, and the same thing is true with Robust.

You always have to continue developing [and] look for more things, Singer said.

That means identifying new AI-related threats or errors to check for, and then updating the technology accordingly. Singer said the company issues a new release every six weeks.

Depending on the contract that we have with an organization, we make the updates available for customers if they wish, he said.

As insurers and other industries keep turning to AI for at least some parts of their operations, demand could rise for companies such as Robust to help sift out errors those systems can unintentionally generate, Singer said.

As more and more companies are relying on AI to help them automate decision-making, the risks increase, Singer said, there really needs to be testing as well as a safeguard right on these [AI] models, and thats exactly what Robust Intelligence provides.

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How one firm takes insurer's AI to the next level - Insurance Business

NHS England is working with the Ada Lovelace Institute to pilot algorithmic impact assessments (AIAs) in healthcare. The Ada Lovelace Institute defines AIA as a tool for assessing possible societal impacts of an algorithmic system before the system is in use.

NHS England said the pilot, run by NHS AI lab, will be used as part of the data access process for the National Covid-19 Chest Imaging Database (NCCID) and the proposed National Medical Imaging Platform (NMIP).

While artificial intelligence (AI) has the potential to support health and care workers to deliver better care for people, it could also exacerbate existing health inequalities if concerns such as algorithmic bias arent accounted for. In what is believed to be a world first for AI adoption in healthcare, the trials goal is to ensure potential risks such as algorithm biases are addressed before they can access NHS data.

Through the trial, NHS AI Lab said that it will support researchers and developers to engage patients and healthcare professionals at an early stage of AI development when there is greater flexibility to make adjustments and respond to concerns.

Brhmie Balaram, head of AI research and ethics at the NHS AI Lab, hopes that supporting patient and public involvement as part of the development process will lead to improvements in patient experience and the clinical integration of AI.

Building trust in the use of AI technologies for screening and diagnosis is fundamental if the NHS is to realise the benefits of AI, said Balaram. Through this pilot, we hope to demonstrate the value of supporting developers to meaningfully engage with patients and healthcare professionals much earlier in the process of bringing an AI system to market.

NHS AI Lab commissioned The Ada Lovelace Institute to provide a guide on how to use an AIAs in the real-world. It is designed to help developers and researchers consider and account for the potential impacts of proposed technologies on people, society and the environment.

The sample version of the process guide, available to download from its website, provides step-by-step guidance for project teams seeking access to imaging data from the NMIP. It outlines how to conduct an algorithmic impact assessment (AIA) for their project.

The guide is aimed at designers, developers, data scientists and product or research managers working on products and data models that need to use NMIP imaging data. It covers accountability, transparency, record keeping and critical dialogue on how the design and development of the system might result in particular harms and benefits.

Octavia Reeve, interim lead at the Ada Lovelace Institute, said: Algorithmic impact assessments have the potential to create greater accountability for the design and deployment of AI systems in healthcare, which can in turn build public trust in the use of these systems, mitigate risks of harm to people and groups, and maximise their potential for benefit. We hope that this research will generate further considerations for the use of AIAs in other public and private-sector contexts.

Innovation minister Syed Kamall said: While AI has great potential to transform health and care services, we must tackle biases which have the potential to do further harm to some populations as part of our mission to eradicate health disparities.

This pilot once again demonstrates the UK is at the forefront of adopting new technologies in a way that is ethical and patient-centred.

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NHS England works with Ada Lovelace Institute to tackle AI bias in healthcare - ComputerWeekly.com

Artist Hidreley Diao uses artificial intelligence to recreate real-life Disney characters, and more (hidreley, Instagram)

Estimated read time: 2-3 minutes

SPRINGFIELD The "Have You Seen This?" articles are usually reserved for great videos. But every once in a while there is something so cool, so interesting, that no video is needed but you still have to see it.

Today is one of those rare occasions.

Have you ever wondered what Ned Flanders or Moe Syslak would look like in real life? Did it ever cross your mind what Ariel or Rapunzel would look like if you saw them walking down the street? Or maybe you've been curious what it would be like to meet Carl Fredrickson in person? Wonder no more, because we now have a pretty strong idea thanks to artist Hidreley Diao.

According to Diao's Instagram bio, he is a contributor for the online art magazine, Bored Panda. The Brazilian artist started experimenting with artificial intelligence and found he could recreate lifelike images of cartoon characters and started to get to work.

On his Instagram page, Diao has portraits of everyone from Dash in "The Incredibles," to the Statue of Liberty. These portraits are mesmerizing, and it's far too easy to fall into a black hole of time-wasting as you scroll and click on every single image.

Some of my favorites include Milhouse from "the Simpsons," composer Frdric Chopin, and the female pharaoh Hatsheput.

Take a look at the portraits, but do yourself a favor: do it when you have some time and your boss won't catch you, because you'll be stuck browsing for awhile.

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Have You Seen This? Artist uses AI to recreate real-life Disney, and more - KSL.com

My mission for more than two decades has been to help artificial intelligence (AI) work for the masses, and I truly believe in AI's potential to make our lives healthier, happier and more productive.

Of course, AI comes with certain challenges (like any emerging technology), especially as companies more fully operationalize AI. In fact, there are at least three significant AI trends already on the move this year -- and I'm closely watching how these shifts will help businesses continue to navigate AI hurdles like removing bias and building trust.

Trend 1: Actioning AI ethics and governance

For years, there's been discussion about eliminating bias from AI models. These concerns are not only prominent among industry professionals -- they're increasingly arising in mainstream media outlets as well. For example, NBC recently aired an episode of "American Auto" that centered on a self-driving car's failure to recognize and brake for people of color.

In 2022, we're seeing conversations about AI ethics and bias mitigation transition from abstract frameworks into real-world practices. This evolution is largely powered by emerging startups that provide AI monitoring and governance solutions for businesses. Now, a big question mark for AI-driven companies is whether to outsource machine learning performance monitoring to companies like Credo, Fiddler and Arize AI, or build out internal capabilities to validate, monitor and analyze machine learning models.

My advice: Don't overthink this decision. At Smart Eye, we have largely implemented checks and balances in house, but I look forward to exploring potential partnerships with emerging companies in the governance space. However, if your organization currently lacks the in-house expertise to properly operationalize AI ethics and governance, go ahead and bring on a partner who can. Many third-party solutions can implement systems that can train, validate and analyze the efficacy of your AI systems, and at levels that are difficult to achieve with data offered by your customer base alone. Start simple as well, perhaps by monitoring the diversity of your training and test data, or biases present in your inference results. Then you can work off of this information to intervene as required.

Over time, you can increase efforts and adopt more tools and capabilities to help eliminate bias and add model explainability. From my perspective, the important thing is that your organization is bringing AI ethics and governance into action now.

Trend 2: Increasing AI's role in hybrid workplaces

According to recent research from Microsoft, more than 70% of workers globally want flexible remote work options to continue. I know that's how I personally feel. Hybrid work environments are here to stay -- even at my company, we're figuring out what hybrid looks like in practice, and how to get ahead of employee burnout. But one thing I'm sure of is that AI will continue to drive innovation in the future of work.

We've seen a recent rise in organizations embracing collaboration and workspace tools designed to boost engagement and happiness levels. As a next step, layering in AI can help you learn so much more about how your team is doing. For example, I'm a big fan of startups like Read AI, which monitor meetings in real time to gauge who speaks the most, the overall sentiment in the "room" and other nuanced behaviors. Over time, gathering this information helps leaders improve future experiences for employees, surface helpful coaching insights and uncover team members' skills.

The pandemic has accelerated the adoption of virtual and/or hybrid settings, and it's great to see more tools coming to market that can quantify and support social and emotional intelligence in organizations. Upgrading how you engage with employees has powerful mental health implications, too. Rightfully so, building out more helpful mental health resources remains a top priority for organizations in 2022 -- nearly 40% of employers expanded mental health benefits during the pandemic. I'm eager for more large-scale deployment of AI systems that better quantify an individual's mental health needs and can provide just-in-time support -- stay tuned for more updates on that front!

Trend 3: Exploring AI and Web3

A third trend I'm keeping my eye on is the intersection of AI and the emerging world of Web3, crypto and NFTs (non-fungible tokens).

One obvious area where AI is being applied is in synthetic data -- otherwise known as artificially created data. In my world, we turn to synthetic data all of the time to power deep learning models and train generative models -- without having to dedicate massive amounts of time and money to producing labeled diverse data sets. We're slowly seeing the application of generative adversarial networks in Web3, where one can create thousands of unique, synthetic characters to populate the metaverse. This opens the door to engineering new user experiences, and even exploring new monetization and branding opportunities (think influencers in the metaverse!).

The same goes for NFTs. While NFTs themselves still feel quite new, there are a lot of opportunities to embed AI and make these digital assets more interactive. Imagine intelligent NFTs (iNFTs) that have natural language understanding, perceptual capabilities and computer vision, and therefore can engage audiences in conversations -- an iNFT telling you about its "origin story," for instance. This is definitely a space I'm watching.

No matter the trend, keep AI human

Amid divergent trends, there's a consistent element in 2022: keeping humans central to the AI equation.

This goal is giving way to the emergence of a new technology category: human insight AI -- AI technologies designed to understand, support and predict human behaviors within complex environments. This year, we're already seeing human insight AI strategies leading to improvements, from the interiors of our cars to hybrid work environments ... and maybe even in the metaverse. But no matter where human insight AI is applied, we will all be better off for it, and I can't wait for these human elements to power all sorts of AI experiences in 2022 and beyond.

The opinions expressed here by Inc.com columnists are their own, not those of Inc.com.

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3 AI Trends to Watch in 2022 | Inc.com - Inc.

Artificial intelligence is among the most poorly understood technologies of the modern era. To many, AI exists as both a tangible but ill-defined reality of the here and now and an unrealized dream of the future, a marvel of human ingenuity, as exciting as it is opaque.

Its this indistinct picture of both what the technology is and what it can do that might engender a look of uncertainty on someones face when asked the question, Can AI solve climate change? Well, we think, it must be able to do something, while entirely unsure of just how algorithms are meant to pull us back from the ecological brink.

Such ambivalence is understandable. The question is loaded, faulty in its assumptions, and more than a little misleading. It is a vital one, however, and the basic premise of utilizing one of the most powerful tools humanity has ever built to address the most existential threat it has ever faced is one that warrants our genuine attention.

Machine learning the subset of AI that allows for machines to learn from data without explicit programming and climate change advocacy and action are relatively new bedfellows. Historically, a lack of collaboration between experts in the climate and computer sciences has resulted in a field of exploration that is still very much in its infancy.

Happily, recent years have seen the beginnings of a shift in that paradigm, with groups like Climate Informatics and the Computational Sustainability Network focusing on how computational techniques can be leveraged to advance sustainability goals.

Taking this notion a step further, a group of young experts in machine learning and public policy founded Climate Change AI in 2019, a non-profit that aims to improve community-building, facilitate research and impactful work, and advance the machine learning-climate change discourse.

There have been different communities working on different aspects of this topic, but no one community unifying the discourse on AI and the many different approaches to climate action, explained Priya Donti, co-founder and power and energy lead of CCAI in an interview with Interesting Engineering.

Climate Change AI has, in no uncertain terms, altered that landscape. In 2019, the group published a paper entitled Tackling Climate Change with Machine Learning, a call-to-arms for the machine learning community that presented 13 areas ranging from electricity systems and transportation to climate prediction and agriculture where the technology might be best utilized. Dozens of experts in the machine learning, climate change, and policy communities contributed sections to the paper and well-known figures like Andrew Ng and Yoshua Bengio provided expert advice on the project as well.

In the years since its publication, the organization has helped foster communication through workshops and other activities, ensuring that the people joining these events are a blend of computer scientists and those from other disciplines.

Encouraging this communication is neither easy nor without its difficulties, however, something that David Rolnick, one of the papers authors and co-founder and biodiversity lead of CCAI readily acknowledges.

The machine learning and AI community is very vulnerable to hubris, explained Rolnick in an interview with Interesting Engineering. Thinking we can solve the problems of other fields without [...] working with people in those fields, without having to leave our algorithmic tower. As in other areas of applied machine learning, meaningful work on climate change requires collaboration.

The interdisciplinary mingling the group promotes is beginning to bear fruit. Many of the professionals who engage in these events help facilitate dialogue between experts of varying fields who would otherwise have a hard time understanding each other, a prerequisite of any collaborative effort.

Were starting to see a lot more people who [...] are not 100 percent machine learning experts, theyre not 100 percent experts in the climate-change-related domain, [but] theyve done a really good job of doing work at the bridge between those two things, and as a result, are able to bring people together, Donti notes enthusiastically.

The team at CCAI believe that researchers and policymakers alike are beginning to alter the focus of their efforts as a direct result of the groups 2019 paper, and its broader efforts. Along with healthcare, climate change is now widely viewed as a key application of AI for the greater good, something that wasnt the case just a few years ago.

I think one thing thats inspiring is the number of people who have risen up to take on [the climate change] challenge, says Donti.

Crucially, though, that inspiration needs to translate to results, and that mentality underpins the teams efforts.

Whether Im optimistic or pessimistic, fundamentally, Im action oriented, and I think its important to do what we can, she underscores.

Ultimately, doing what we can to address climate change through AI (or any other technology) is going to be approached via two basic principles: limiting greenhouse gas emissions going into the future and responding to the effects of what levels of climate change we have, unfortunately, already locked in.

Research bodies, governmental institutions, and private companies around the world are beginning to take up the challenge on both fronts. Brainbox AI, for example, is a Montreal-based company that uses machine learning to optimize HVAC systems in office buildings and other kinds of real estate. This is a key area to focus on when dealing with potential GHG reduction, as the energy consumed by buildings accounts for a quarter of global energy-related emissions alone.

Given that real estate is a major contributor to greenhouse gas emissions, the decision-makers in the industry have a major opportunity to lead the charge, explained Jean-Simon Venne, CTO and co-founder of Brainbox AI in an email exchange with Interesting Engineering.

An AI-driven HVAC system can allow a building to self-operate, proactively, without any human intervention. It can ultimately evaluate the most optimal HVAC configuration for energy efficiency, saving money but also reducing the load on the power grid, keeping the buildings footprint low.

Adaptation will be just as crucial an effort, as extreme weather events driven by rising temperatures rapidly increase in frequency. Disaster response is one area already seeing the application of AI technologies, with machine learning being used to help people recover from natural catastrophes far quicker than in the past.

Such was the case during the 2021 typhoon season in Japan, when the U.K.-based company Tractable used its AI in partnership with a major Japanese insurer to assess external property damage caused by Typhoon Mindulle, helping homeowners recover more quickly. The company claims it can reduce the time needed for damage assessment from several months to a single day.

Just as neither of the goals of climate change mitigation and adaptation will be easy to make progress with, neither can be accomplished using AI alone. While the technology lends itself to flashy news headlines and compelling sci-fi narratives in literature and film, its far from the silver-bullet solution that its often made out to be.

Rolnick stresses that the practicality of what machine learning can and cant accomplish must be a primary consideration when entertaining the idea of applying the technology to any particular problem. Climate change isnt a binary issue, and we must mould our attitudes accordingly.

[AI] is not the most powerful tool, he emphasizes. Its not the best tool. Its one tool, and its a tool that I had at my disposal. Im not optimistic because of AI specifically, Im optimistic because climate change isnt an on-off switch. We get to decide just how bad it is. Any difference that we can make is a meaningful difference that will save lives.

The applications of machine learning are manifold, and both the groups 2019 paper and their recently-published policy report for the Global Partnership on AI are well worth an in-depth read.

The team at CCAI underscores that one basic use of machine learning in this space is its ability to help gather data, like how the technology was recently used to create a map of the worlds solar energy facilities, an inventory that will be of great value going into the future. Such datasets will help scientists better guide their research and policymakers make informed decisions.

Another area where it can make a substantial difference is in improving forecasting, scheduling, and control technologies that pertain to electricity grids.

The energy output of electricity sources like solar panels and wind turbines are variable, meaning they fluctuate depending on external factors like how much the sun is or isnt shining on any particular day.

To ensure consistent power output independently of weather conditions, back-ups like natural gas plants run in a constant CO2-emitting state, ready to fill in those gaps. Improving energy-storing tech like batteries could be a way to reduce the need for such high-emission practices, with machine learning being able to greatly accelerate the process of materials development and discovery.

Were seeing huge advancements in batteries in terms of cost and energy density, Donti says. Batteries are going to be a critical piece of the puzzle, and there are some companies using AI to speed up the discovery of next-generation batteries. One example is Aionics.

Aionics is a U.S.-based startup using machine learning to expedite battery design, which could, in addition to improving electricity systems, unclog one of the bottlenecks standing in the way of electric vehicle adoption on a large scale.

Using machine learning to help decarbonize the transportation sector on a larger scale is more difficult, however. Passenger and freight transport are notoriously difficult to decarbonize. If fossil fuels are to be replaced with batteries, for example, they will in many cases need to be extremely energy-dense. But thats only a tiny part of the picture, the bigger issue being the convoluted nature of the transportation sector itself.

In the electricity sector, you have relatively few, large players, and its rather centralized. What happens in terms of innovations is happening in fewer companies with more aggregate datasets, explained Lynn Kaack, assistant professor of computer science and public policy at the Hertie School in Berlin and co-founder and public sector lead at CCAI in an interview with Interesting Engineering.

In transportation, there are many more and smaller companies [...] often there is much less means, much less data to exploit. Where one can take the system perspective, trying to optimize routing, charging station placement, machine learning has interesting things to add, but its not always straightforward.

Kaack points to the example of how German passenger rail operator Deutsche Bahn is looking at maintenance optimization through machine learning. Technological failures result in delays, and delays have a big influence on whether or not passengers perceive rail as a viable alternative to driving.

Technical challenges are far from the only thing that needs to be overcome in the service of doing right by the planet. How these issues and their potential solutions are framed and perceived matters greatly.

The public sphere is prone to putting a spotlight on glitzy techno-cures that can divert attention away from simpler but potentially more actionable projects and technologies. Neither are research bodies or governmental agencies immune to such frenzy. Awareness here is crucial, as the lens through which AI is seen can play a role in dictating the direction research leans and where funding ends up.

AI can make certain kinds of action easier, but it can also lead to greenwashing, Rolnick warns. Techno-solutionism can lead people to think they are having a much bigger impact than they are, and even divert peoples attention away from lower-tech, but more impactful courses of action.

Working on unsexy problems is important. How even the most exciting technologies get integrated into the workflow where they will be applied is quite simply boring, essential work. Persuading the relevant parties involved in funding and finding a new solution often requires the right rhetorical touch.

For different innovations and solutions, we should think about who the audiences are who need to be convinced, who are the people who might be financing things, how do you make [the incentives] clear to private and governmental funding sources, Donti says.

By the looks of things, many appear to find the group and its goals compelling. Climate Change AI has had a direct impact on funding for programs like the U.S. governments DIFFERENTIATE program and Swedens AI in the service of the climate program, for example, and theyve just finished the first round of an innovation grants program thats allocating two million dollars to projects that will promote new work by creating publicly available datasets.

On a broader scale, how we leverage and manage AI is a topic that is increasingly being given the attention it deserves. Last April, the European Commission introduced the Artificial Intelligence Act, the first large-scale regulatory framework for the European Union regarding technology.

While some claim the framework doesnt do enough to protect civil rights and liberties, it is a step in the right direction, and the more central and common these high-profile discussions become, the better. Anyone and everyone involved in machine learning applications need to embed the ethical considerations of relevant stakeholders, not just investors, into the foundations of the technology as much as possible.

Taking all of this together, its not a stretch to say that AI can be utilized to address climate change. But the fact remains that the issue is an extraordinarily complex one, and even those directly involved in approaching it admit that the conversation of when and how we do that is an ever-evolving one, wherein the most effective path forward is never exactly clear.

Are you going to spend your time with practical applications and policymaking, helping people who are supposed to make decisions shape funding programs and inform legislation, or do you go back to fundamental research? Its difficult to balance them and understand which has the greatest impact, Kaack says.

While a difficult question to navigate, that its even being asked is nothing short of inspiring. Doing what is within ones reach stands out as an evergreen principle for achieving real, tangible action, even when dealing with something like climate change. The overall message is less of a, Do it with AI, and simply more of a, Do, period. In the face of a problem of this scale, one that often feels paralyzing in its insurmountability, that message is a refreshingly galvanizing one to hear.

Im not here to say that AI should be our priority, reiterates Rolnick. AI is a powerful tool, but climate action will require all the tools. The moral of the story for me is that it is important for people to think about how they can use the tools they have to make a difference on problems that they care about.

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Can AI Save Humanity From Climate Change? That's the Wrong Question - Interesting Engineering

Israeli predictive analytics platform, Pecan AI, has secured $66 million in a Series C funding round led by global private equity and venture capital firm, Insight Partners, along with support from prior investors including GV, S-Capital, GGV Capital, Dell Technologies Capital, and others.

This raises the companys total funds raised to over $100 million.

Pecan AI said the funding will be used to scale its global footprint and accelerate research and development of thelow-code predictive modeling and data science platform.

Founded in 2018, Pecans AI services aid business intelligence, operations, and revenue teams to predict revenue-impacting risks and outcomes without the need for data scientists. Pecan enables its users to transform substantial amounts of raw transactional data into accurate insights predicting the profitability impacts key performance indicators such as customer lifetime value, retention, conversion rates, and demand forecasting directly yield.

To date, Pecans predictive algorithms impact billions of dollars in revenue for consumer goods, fintech, insurance, mobile application, and wellness and beauty companies of various sizes, tripling its annual revenue from the past year.

We believe that any company should be able to deploy AI-based predictive analytics, even without data science resources on staff, said Zohar Bronfman, CEO and co-founder of Pecan AI. This new funding will help us scale Pecan further to overcome the data science scarcity gap, enabling our customers to move beyond outdated data-mining techniques that offer little value in predicting future outcomes.

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Pecan AI Raises $66M To Advance AI Automation And Predictive Analytics - NoCamels - Israeli Innovation News

Magnetic resonance imaging (MRI) technology is a widely used albeit costly tool for diagnosing brain injuries and strokes. Its high procurement, installation and operating costs, however, mean much of the developing world has no access to it.

Researchers from the University of Hong Kong (HKU) have successfully developed a new magnetic resonance imaging (MRI) technology, the ultra-low field (ULF) 0.055 Tesla brain MRI, which can operate from a standard AC wall power outlet and requires neither radiofrequency nor magnetic shielding room. Further, a conventional, typical MRI machine can cost up to US$3 million, yet the ULF-MRI scanner costs only a fraction of this price.

The research team was led by Professor Ed X. Wu, Chair of Biomedical Engineering and Lam Woo Professorship in Biomedical Engineering of the Department of Electrical and Electronic Engineering, HKU. The research output was published inNature Communications, and also highlighted inNature AsiaandScientific American.

The HKU team is one of the three leading ULF-MRI academic research groups worldwide, with one based at Harvard/MGH, dedicated to developing novel ULF-MRI technology. Their goal, as shared by researchers like Professor Wu, is to popularise and broaden the use of MRI.

As an MRI researcher for over 30 years, Professor Wu is delighted and derives a strong sense of fulfilment from the development of what he calls a scaled-down MRI scanner that is far more affordable than what is on offer in hospitals. The human body is mostly made of water molecules, on which MRI thrives, said Professor Wu. MRI is a gift from nature and we must use it more. Currently, it is underutilised as a diagnostic tool.

It is estimated that currently more than 90% of MRI scanners are located in high-income countries, and two-thirds of the worlds population do not have access to them. The total number of clinical scanners is estimated at only about 50,000 worldwide.

The HKU team has made the design and algorithms of ULF 0.055 Tesla brain MRI open-source knowledge, available to all interested in developing the technology further or applying it in diverse areas. This virtually opens the door to making advancements in various aspects of healthcare provision in terms of MRI applications. This will be a big field, Professor Wu said, the team has demonstrated the concept and shown the feasibility of a simplified version of MRI. There are many ways to move forward.

With the use of a deep learning algorithm, the team has removed the constraint in conventional MRI, namely the need to be shielded from the outside radiofrequency signal, which results in a bulky, non-mobile set-up. The existing MRI scanners are essentially giant magnets and need a purpose-built room to shield them from outside signals and to contain the powerful magnetic fields generated by their superconducting magnets, which require costly liquid helium cooling systems. The teams new computing and hardware concept made the latest development possible.

Professor Wu is confident that a critical mass of researchers could push the frontiers of knowledge. He noted that the open-source approach is the quickest way to spread knowledge. It is hoped that MRI can be used in more fields other than radiology, for example in paediatrics, neurosurgery or the emergency room. The team welcomes more people from the scientific, clinical and industrial sectors to research to benefit healthcare, he said.

In collaboration with Professor Gilberto Leung of Neurosurgery and other clinicians at Queen Mary Hospital, his team had validated the results of using ULF-MRI by comparing them with images obtained from a standard 3 Tesla MRI machine. They could identify most of the same pathologies, including stroke and tumours results, despite the lack of clarity and resolution required for precision diagnostics.

Professor Wu said, I believe computing and big data will be an integral as well as inevitable part of the future MRI technology. Given the inherent nature of MRI, I believe widely deployed MRI technologies will lead to immense opportunities in the future through data-driven MRI image formation and diagnosis in healthcare. This will lead to low-cost, effective, and more intelligent clinical MRI applications, ultimately benefiting more patients.

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Improving AI-enabled Healthcare in the U.S. - OpenGov Asia

Todays technological advancements have simplified our lives to the point where everything from email responses to social media postings can now be completely automated with the help of a scheduling software that leverages artificial intelligence.

Had the Covid-19 pandemic hit in the 1990s, businesses and peoples lives would have been more adversely affected, while the economic damage would have been astronomical.

However, todays innovative technologies have offset much of the pandemics damage to our economy. Over the past two years, for instance, I have led remote teams and initiated and executed multiple projects often without physically meeting my clients.

But even with all of these technological advances, one thing that AI will not be able to replace is our creativity; the compilation of our complex emotions, thoughts and aspirations.

AI has the ability to process information that is already out there. While it is good at replicating repetitive or predictive tasks, creativity is deeply rooted in the human experience and out of reach for AI.

The spark of inspiration that prompts artists or designers to think of a new future, a new solution or come up with an art piece that connects people across time and space, is something that AI cannot compete with.

Creativity is, without a doubt, the most coveted skill of the future and one that businesses and governments need to incorporate in their processes to stay ahead.

As an Arab society, creativity and creatives have always been held in high regard. Poetry, for example, had a special place in the Holy Kaaba during the pre-Islamic period.

Poems recited by the greatest Arab poets of the time, such as Antarah ibn Shaddad and Labid, have been written in gold on strips of Egyptian cotton and suspended from the interior walls of the Holy Kaaba. The creativity of poets was also celebrated annually at Souk Okaz, in Taif, Saudi Arabia, where Arabs would gather from faraway lands to recite poetry and sell their merchandise.

The appreciation of poetry and poets not just then, but even now is testimony to how we as a society have always valued creativity. We now see that passion and respect for creativity and creatives reflected in our national policies.

The Dubai Creative Economy Strategy, for instance, aims to double the contribution of the creative sector to the gross domestic product of Dubai to 5 per cent by 2025, from 2.6 per cent in 2020.

Meanwhile, the UAEs National Strategy for Cultural and Creative Industries (CCI) announced last December that it will introduce 40 initiatives across three segments in the creative sector: talents and creatives, professionals and business environment, and enabling of the business environment.

The strategy establishes a new phase in the future of the creative economy and lays down a strong foundation to enhance the contribution of the cultural and creative sector to achieve sustainable development, Noura Al Kaabi, Minister of Culture and Youth, said at the time.

The work of creatives inspires us and drives us to change, to dream and to build a better future. The technology race will not slow down. A year from now, the way we work may completely change. Technology is likely to be so entrenched in our lives that we may realise theres no area left where it does not dominate.

But creativity is what will help us stay ahead of the game. For that to continue, we need to incorporate creativity in our decision-making processes for it to play an active role in building the future and helping us come up with solutions to global issues.

Creativity will always be the distinguishing factor the one thing that AI will never be able to overcome.

Manar Al Hinai is an award-winning Emirati writer and communications consultant based in Abu Dhabi. Twitter: @manar_alhinai

Updated: February 7th 2022, 3:30 AM

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Why artificial intelligence will never replace creativity - The National

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Political (Political policy and stability as well as trade, fiscal and taxation

policies)

Economical (Interest rates, employment or unemployment rates, raw material

costs and foreign exchange rates)

Social (Changing family demographics, education levels, cultural trends,

attitude changes and changes in lifestyles)

Technological (Changes in digital or mobile technology, automation, research

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Legal (Employment legislation, consumer law, health and safety, international

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Environmental (Climate, recycling procedures, carbon footprint, waste

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This Artificial Intelligence (AI) in Security Market report studies the top producers and consumers, focuses on product capacity, value, consumption, market share and growth opportunity in these key regions, covering

1. North America (United States, Canada and Mexico)

2. Europe (Germany, France, UK, Russia and Italy)

3. Asia-Pacific (China, Japan, Korea, India and Southeast Asia)

4. South America (Brazil, Argentina, Colombia etc.)

5. Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria and South Africa)

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Artificial Intelligence (AI) in Security Market Expected to Witness a Sustainable Growth over 2026 | Amazon.Com, Inc., Fortinet, Google (Alphabet...

Early evidence suggests chatbots could play a role in reversing negative hiring trends and jump-starting the countrys economic recovery.

Ten years ago, Georgia State University was facing a challenge that may seem strange on its surface. A surprisingly large number of admitted students around 20 percent, all of whom had indicated their intention to enroll werent showing up for the first day of class.

This phenomenon, which has come to be known as summer melt, wasnt just an issue at Georgia State. Research suggests up to40 percentof students from low-income communities who are accepted to college never matriculate. For many of those students, the maze of paperwork and other requirements not to mention the fact that they are often the first in their families to attend college and may lack the sort of support so many college students take for granted puts an end to their educational aspirations, before they even arrive on campus. It may go without saying that the pandemic has only exacerbated the challenge.

But the story of summer melt is not limited to higher education. In the world of work, theresevidencethat so-called ghosting, in which workers who have accepted a job stop engaging with their employer before their first day, has been on the rise. One study conducted in 2020 found 28 percent of job seekers report ghosting an employer, up from just 18 percent the previous year. And that was before the Great Resignation, which has seen more workers quit their jobs in a concentrated period than ever before.

While the names may be different, the challenge is similar: too many people arent taking the next step in their personal journey, be it educational or professional. What are the barriers standing in their way and what can be done about them?

As it turns out, colleges and universities may have something to teach the business community. The same tools being used to address summer melt hold the potential to tackle ghosting in the workplace.

In 2016, Georgia State undertook a first-of-its-kind experiment: using artificial intelligence to help students navigate the transition to college. Accepted students were contacted via text message by a chatbot named Pounce (named for the schools mascot), which proactively reminded them about key deadlines and provided guidance to make sure they completed all the requisite tasks. The results speak for themselves. According to arandomized controlled trial, students who received the outreach were 3.3 percentage points more likely to enroll in the fall. That represents hundreds, if not thousands, of students who owe their educational experience to the support of an AI chatbot.

Since that study, many more colleges have followed in Georgia States footsteps, as have states likeWashingtonandTexas, and nonprofit organizations like theCommon App. Using a framework calledbehavioral intelligence, which brings together 24/7 support with personal and contextual understanding, these partnerships are demonstrating the potential of AI to help students take the next step in their journey to and through college.

With these promising results, it stands to reason the approach may work for entry-level workers much the same way it does for incoming college students. What would that strategy look like in practice?

Over the past year, a growing number of employers havebegun experimentingwith the application of AI and behavioral intelligence to address ghosting and help smooth the path into a new job. So far, the results are promising. While the projects are just in their pilot phase now, theyve demonstrated measurable decreases in ghosting and helped more early-career workers start their job on the right foot.

At a time when the so-called Great Resignation is upending the labor market as we know it, approaches such as these have never been more important as a way for employers to connect and communicate with incoming talent more effectively.

Just as importantly, AI is beginning to help employers listen at scale. Consider the results of one survey that a chatbot issued to jobseekers about why they dropped from the hiring process. For many respondents, logistics were the greatest challenge: one person missed an internet speed test and couldnt reschedule and another received word that their background check never came back. These seemingly small things made the difference between starting a new job and starting over from scratch. And both are the exact sort of challenge that AI has helped to address in the higher education context.

While theres still much more to learn, its clear AI can make a difference for people who need the right nudge, at the right time, to continue their progress, whether educational or professional. If these pilot programs in the workforce context prove effective, as they initially seem to be, how could chatbots play a role in reversing negative hiring trends and jump-starting the countrys economic recovery?

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Can AI solve ghosting in the workforce? - Talent Management