Category: Technology

In a discussion paper, UK financial supervisory authorities assessed the benefits, risks and harms of the current legal framework applicable to artificial intelligence in financial services.

UK financial services regulators, the Bank of England (BoE), the Prudential Regulatory Authority (PRA) and the Financial Conduct Authority (FCA) – together the supervisory authorities – have published a joint discussion paper (DP5/22) on artificial intelligence (AI) and machine learning. On 11 October 2022. The discussion paper was intended to facilitate public debate on the safe and responsible adoption of AI in financial services in the UK.

Essentially, the discussion paper examines:

• potential advantages of providing a regulatory definition of artificial intelligence;

• the benefits, risks, and harms related to the use of artificial intelligence and machine learning that could significantly affect or even change how financial services and markets operate;

• How the current regulatory framework can be applied to artificial intelligence.

Possible risks and damages

The supervisory authorities also raised discussion questions for stakeholder input, with the aim of understanding whether the current regulatory framework is sufficient to address the potential risks and harms associated with AI and how any further intervention could support safe and responsible adoption of AI in the UK. Financial Services.

The discussion paper provides a platform for supervisory authorities, experts and stakeholders to collaborate and assess whether the current legal framework can adequately regulate AI technology by protecting the objectives of both supervisory authorities while promoting innovation in UK financial services.

This consultation occurs in parallel with the UK Government’s ongoing work to develop its cross-sectoral approach to regulating AI technology, and will therefore provide a valuable contribution to this broader policy debate.

Define artificial intelligence

Supervisory authorities suggest that there are benefits to a precise definition of AI which include: creating a common language for companies and regulators, which may mitigate uncertainty; Assist in a unified and coordinated response of regulators to AI; and provide a basis for determining whether or not specific use cases can be registered under certain rules and principles.

The benefits and risks of using AI are categorized in the discussion paper based on the objectives of each of the supervisory authorities, namely consumer protection, competition, corporate safety and integrity, protection of policyholders, financial stability and market integrity.

The supervisory authorities have also provided current and future legal requirements and guidance relevant to mitigating the risks associated with AI, including but not limited to the FCA Consumer Duty Rules, the UK General Data Protection Regulation (UK GDPR), the Equality Act 2010 and the Senior Managers System. Accreditation (SM&CR).

Total IT spending worldwide is expected to reach $4.6 trillion in 2023, a 5.1% increase over 2022, according to Gartner’s latest forecast.

Analysts House expects companies to “move forward with digital business initiatives,” even though economists around the world have predicted a possible recession on the horizon.

For example, economists from the International Monetary Fund expected an “increasingly bleak and uncertain outlook”, with Core international economic growth forecast to slow down (Opens in a new tab) From 6.1% in 2021, to 3.2% in 2022, and just 2.9% in 2023.

Where will spending go up the most?

Gartner warns that not all sectors are set for equal growth, and hardware spending is likely to be the worst performing of all the IT sectors examined.

Analysts’ house forecast hardware spending will fall -0.6 to $735,394 million in 2023, a sharp drop from the 15.8% growth recorded in 2021.

The software market appears to be one of the strongest performing IT segments, growing by 11.3% in 2023 to reach $879,625 million, which the company attributed to a “switch to cloud options.”

The impressive performance of the software sector can be related to the constant demand for possible tools video conferencing And the Remote Collaboration after the epidemic.

IT services are set to rank as the second best performer, growing 7.9% in 2023 to reach $1,357,914 million in 2022.

Data center systems and communications services are also poised for solid growth if Gartner’s statistics are to be believed, with growth of 3.4% and 2.4%, respectively, expected in those regions.

“Enterprise IT spending is stagnating as CEOs and CFOs, rather than cutting IT budgets, increase spending on digital business initiatives,” said John David Lovelock, Gartner Senior Vice President Analyst. “The economic disruption will change the context of technology investments, increase spending in some areas and accelerate declines in others, but it is not expected to have a material impact on the overall level of enterprise technology spending.”

However, inflation has eroded the purchasing power of consumers in almost every country around the world. Consumer purchasing power has been reduced to the point that many consumers are now delaying the purchase of devices for 2022 until 2023, which has reduced spending on devices by 8.4% in 2022 and 0.6% in 2023.”

• Interested in reducing your IT spending? Check if our list for The best cloud storage Can provide any suitable options.

The relationship between restaurants and service organizations is a story as old as time. The ice cream machine breaks down, the restaurant manager contacts the service company, they send a service technician to fix the machine, the technician fixes the problem, and customers enjoy their soft service again. Easy enough, right? If only it were that easy.

Due to labor shortages and skill gaps, it’s time to stop kitchen tools – Like the above ice cream machine – it gets longer. Restaurant owners and food service managers are going to great lengths to recruit and train an unprecedented level of workers to fill vacancies, causing repair and maintenance to take a back seat. At the same time, the service industry’s aging workforce makes it difficult to find qualified technicians, which leads to longer response times when a service call comes in from a restaurant.

Restaurants have few employees, and this puts other industry challenges in the back seat

Labor shortage remains a major challenge facing many business owners. July 2022 survey From the National Federation of Independent Business It found that about half of small business owners said they were still unable to fill vacancies, a near-record level in the survey’s nearly five-decade history.

Restaurant owners, as we know, are among this group – frantically searching for new workers, with exponentially increased time and effort to train their new workforce. The industry is still down 750,000 jobs — roughly 6.1% of its workforce — from pre-pandemic levels as of May 2022, according to the National Restaurant Association. As a result, repair and maintenance of kitchen equipment has become an afterthought.

However, putting repair and maintenance (R&M) planning on the back burner will in turn increase a restaurant’s overall operating and capital expenditures by 15%, according to ResQ trip status Report.

But rather than just prioritizing traditional R&M, it’s time for service and repair teams to embrace the new technologies that will enable the entire workforce to complete jobs more efficiently—no matter how experienced technicians are—so that restaurants avoid downtime altogether and remain fully operational. Restaurant owners can then reallocate time and money to more efficient uses, such as ensuring they can hire staff as needed.

Service organizations are applying artificial intelligence techniques to simplify operations

At the same time, finding qualified service technicians is a major sore point. This issue affects many service companies operating in the food service industry, including United Service Technologies (UST) who serve hot side equipment in supermarket. As an added challenge, floor tank customers are demanding actionable data at an increasing rate. Faced with these two issues, UST turned to artificial intelligence to solve its problems.

Floor cabinets couldn’t find time to manually review thousands of bills to identify useful insights. But now, with powerful emerging technologies, such as AI-powered service intelligence, their teams can organize and analyze bills, calls, and reports in minutes, providing actionable insights in a fraction of the time. Using this emerging technology has helped ground reservoirs create actionable service insights over the 25 years of data they’ve collected.

Ground tanks are now taking advantage of the vast amount of data they have and using it to transform their once reactive business into a more predictive one. They are able to be directive and tell customers what the full life cycle of a machine will look like, all without exhausting their IT efforts.

Floor cabinets also use service intelligence as a learning tool for new technicians to troubleshoot and learn the trade while in the field. Based on historical and real-time data, the system asks technicians a series of questions, then narrows down the most likely scenario, including the root cause of the expected failure, required parts, and any additional or incidental issues. Technicians reach the authority to make the right decisions about alerts. Instead of taking a trial approach and replacing unnecessary parts, or straying from a “no-fault” diagnosis, both novice technicians and service professionals are more likely to complete the maintenance job correctly the first time. Such technology not only eliminates the skills gap between seasoned and novice technicians, but also reduces excessively costly and time-consuming training.

Another company moving the needle with artificial intelligence innovation is smart careAmerica’s leading commercial kitchen service organization.

Leaders at Smart Care realized that they needed to streamline processes once the organization was flooded with data. The single work order that technicians fill out each day contains thousands of data capture points. They needed to figure out how to leverage technology to build a platform that would allow Smart Care to help both customers and technicians alike by gathering data and organizing it in a way that is mutually beneficial.

Smart Care wanted to leverage field service analytics and gain service insights that could help them transition from a reactive legacy service organization to a guiding organization. Smart Care didn’t want to be stuck in an outdated business model, so in order to stay competitive, the company set out to find a solution through technology.

They turned to the intelligence service. The AI-powered tool was not only able to read a work order as a live document, but also analyze field notes captured by each technician on site. With this ability, Smart Care took the most complex pieces of equipment and worked with the manufacturer to obtain building materials and FAQ documents, along with their work order data, to build a diagnostic tool that technicians in the field use on specific pieces of equipment.

As a result, Smart Care has seen significant reductions in things like recall rates and wrong parts ordering which in turn save the company thousands of dollars and generate positive customer experiences.

Average restaurant repair and maintenance costs across the United States 28 billion dollars a year. Regardless of the current state of R&M in your restaurant, it is better to be proactive, rather than reactive. Both Smart Care and UST have been at the forefront with their regulatory innovations. Finding the right service partner who has implemented cutting-edge AI-powered technologies on the restaurant industry can save the restaurant industry billions of dollars by avoiding downtime and making sure every piece of kitchen equipment always works as it should.

Source

when it comes to cyber security In the workplace, younger employees don’t seem to care much, which puts their organizations at serious disadvantage, new research claims.

Professional services firm EY surveyed nearly 1,000 workers using employer-issued devices and found that Gen Z employees were more unconcerned about cybersecurity than their Boomer counterparts in adhering to employer safety policies.

This is despite the fact that four out of five (83%) of those surveyed claimed to understand their employer’s security protocol.

Reuse passwords

When it comes to implementing mandatory IT updates, for example, 58% of Gen Z’ers and 42% of Millennials will ignore them for as long as possible. Less than a third (31%) of Generation X’ers, and only 15% of Baby Boomers said they do the same.

Apathy in youth extends to The password (Opens in a new tab) Reuse between private and business accounts. A third of Gen Z and Millennials surveyed admitted this, compared to less than a quarter of all Gen X’ers ​​and Baby boomers.

Nearly half of Generation Z and Millennials are “likely to accept the web.” Browser Cookies on their devices released from work all the time or often,” compared to 31% of Generation X workers, and 18% of Baby Boomers.

“There is an immediate need for organizations to restructure their security strategy with human behavior at its core,” said EY America’s consulting cybersecurity leader, Taban Shah.

Some say that young people’s apathy towards technology is due to their excessive knowledge of technology, and they have never lived without it.

There is no doubt that being comfortable with technology makes the younger employees of the organization a prime target for cybercriminals looking to exploit any vulnerability in security.

If an organization’s cybersecurity practices are not strongly supported, threat actors can penetrate large networks with simple social engineering attacks.

as it seems from Recent attacks on Uber and Rockstar games (Opens in a new tab)A good social engineer doesn’t have to technically consider leaking sensitive company and customer data, causing hundreds of thousands of dollars in damages in mitigations and fines.

Our exclusive results Password manager Survey in partnership with OnePulse, and we revealed some surprises.

As we all accumulate an increasing number of digital accounts, password security is more important than ever. with advent password generators And the Multifactor authenticationAttempts are made to make our online world safer and more convenient.

However, it seems that for whatever reason, many people are still not fully subscribed and remain restricted to the old ways of managing passwords.

Results

In an unsurprising answer, more than 60% stated that they reuse passwords across multiple accounts. Most did so because they couldn’t be bothered to remember many of them (40%), and some didn’t feel as though they were in danger of being hacked (27%). Only 33% said they did not reuse passwords.

Despite the ease of use of password generators in today’s online world, 65% of them still choose to create their own password generators. Perhaps they were not aware of it, or did not trust them; Or maybe they are worried that if they can’t access the saved passwords, they will be banned from their account, since they are too complicated to save.

Whatever the reasons, it was the most obvious finding in our survey. We probably shouldn’t be too surprised looking at that Most people don’t use password managers also.

Among those who used a generator, most used one integrated with their browser (15%), others used an online generator (13%) and a small minority used one from another source (6%).

People’s diligence seems to differ when it comes to crossing between work and personal passwords: 34% said they don’t share multiple passwords between the two, and 30% said they sometimes do. About 20% each said they did it often or all the time.

Password tip

One of the most important experts in practicing good passwords is Bill Borg, who wrote an influential guide on the subject published by the US National Institute of Standards and Technology (NIST) in 2003. He praised the advantages of creating passwords as random as possible and changing them regularly. Many websites later asked for passwords based on their criteria.

The problem is that in practice, as people collect more and more accounts, they have naturally resorted to simpler passwords. They’ll only tweak it slightly: maybe if you put 1 at the end of your password for one login, you’d probably choose 2 for another, and so on.

Bill Burr came to regret his initial advice. Experts now recommend that when creating your password, using a random but memorable string of three words is best. According to the analysis, These passwords are hard to crack From those who use a single word with a mixture of numbers and special characters, in addition to being easy to remember.

 

The raging wildfires occurring all over the world have caused enormous economic damage and loss of life. Knowing when and where a large-scale fire can occur in advance can improve fire prevention and resource allocation. However, available forecasting systems provide only limited information. Furthermore, they do not provide long enough lead times for useful regional details.

Scientists have now applied a deep learning algorithm to enhance prediction of the danger of wildfires in the western United States. Researchers from South Korea and the United States have developed a hybrid method that combines artificial intelligence techniques and weather forecasts to produce improved forecasts of severe fire danger for one week at finer scales (4 km x 4 km accuracy), increasing its usefulness in fire suppression and management.

“We have experimented with several approaches to integrate machine learning with traditional weather forecast models to improve wildfire risk predictions. This study is a huge step forward because it demonstrates the potential of such an effort to enhance fire risk prediction without the need for additional computing power,” Says lead author Dr. Rackhun Son, a recent Ph.D. from the Gwangju Institute of Science and Technology (GIST) in South Korea, who is currently at the Max Planck Institute for Biochemistry in Germany. “Fire hazard predictions can be further improved using continuous development in both Earth system models and recent AI developments,” He adds.

While data-driven AI methods have shown excellent capabilities to infer things, explaining why and how to come to conclusions remains a challenge. This has led to AI being classified as a black box. “But when artificial intelligence was combined with computer models based on physical principles, we could diagnose what was going on inside this black box,” Says co-author Professor Simon Wang from Utah State University, USA. “AI-based predictions of extreme levels of fire risk are well based on strong winds and specific geographic characteristics, including high mountains and valleys in Western United States that has traditionally been difficult to solve with coarser models. “

Computational efficiency is another major advantage of this method. Conventional methods for predicting fire hazards with precise spatial resolution, a process called “regional minimization,” are often computationally demanding, expensive and time-consuming. “Although comparable computational resources were required in the development phase, once the training task for the AI ​​was completed, i.e. it was done once initially, it only took a few seconds to use this component with the weather forecasting model to produce forecasts for the rest of the season.” says co-author Professor Kyu Sun Lim at Kyungpook National University, Korea. Therefore, the newly developed AI-based method with the ability to make high-accuracy accurate predictions in a shorter time was more cost-effective than traditional forecasting systems.

“In this study, AI is only being tested to predict fire hazard in the western United States. In the future, it could be applied to other types of extreme weather events or in other parts of the world,” Co-author Dr Philip J. Rush from Pacific Northwest National Laboratory and University of Washington. “The flexibility of our AI approach can help predict any weather-related feature.”

The new feature, which was revealed in Microsoft 365 Roadmap (Opens in a new tab)is likely to collect AI-powered “smart summaries” that you provide Microsoft Teams Premium which is the company’s next paid supplement Collaboration platform software.

This is because those summaries, announce At the company’s Ignite event in October 2022, he shared a tentative launch date of December 2022.

Microsoft 365 Productivity Contest

Microsoft’s recent announcement for Outlook is part of the company’s ongoing efforts to make Microsoft 365 More convenient and unified experience

It was also announced on Ignite that existing Microsoft 365 apps across the web and desktop It was withdrawn from service in favor of a single application with a full turnaround scheduled for January 2023.

We first discovered Smart Summary features, which may soon be announced as integrating with other apps within Microsoft 365, with Microsoft أماكن places an app that Microsoft sees as bridging the gap between remote work solutions and the physical workplace, without replacing either.

Microsoft claims the venues will do this by helping employees plan meetings by offering them, among other things, work location labels and a “modern” reservation system.

Taken together, all of these updates can seem exciting to companies within Microsoft’s enterprise ecosystem, and those that embrace the hybrid work environment, but only AI feeds that can be said to innovate and make our lives easier.

In terms of job sites, Google Workspace has long outdone Microsoft, and recently announced that it’s giving IT administrators a choice. Requires That employees use the feature inside Google Calendar.

However, Google itself is playing catch-up when it comes to making sure that users never need More than one application environment.

With both Google and Microsoft scheduling keywords for the same week, offering marginally similar feature sets as part of either company. Collaboration tools (Opens in a new tab)it is not easy to say at present which one is, or will be, more suitable for enterprises.

AI Responsibility Lab PBC, a SaaS company specializing in responsible AI transformation automation, released its latest product today: Mission Control – AI Insights, a new module in its pioneering AI Responsible Transformation platform. AI Insights is the world’s most complete platform for AI risk intelligence and research. It helps legal, financial, and research teams stay on top of the ever-accelerating landscape of AI risks, alleged incidents of harm, and emerging global regulations. AI Insights offers a combination of intelligent automation and advanced filtering that helps teams save hours of discovery and research every week. AI Insights is free to try for 30 days at https://www.takecontrol.ai/insights.

The development and adoption of AI is accelerating and reshaping both industry and society. And it’s not without its downsides: The dangers and harms of AI are emerging as quickly as the technology itself.

What was once apocalyptic science fiction is fast becoming our reality. For example, early October 2022 For the forensic physician United kingdom It included Instagram’s recommendation algorithm as the official listed cause of a teenage girl’s death.

For those who study the dangers of artificial intelligence, this is not surprising. The impacts of AI risks are real, current, and accelerating.

Today, the use of artificial intelligence can exacerbate existing racial and gender biases. The systems that control critical decisions about our lives — such as getting credit and employment — often behave fickle or incomprehensible. Computational optimization was accused of accelerating genocide. There is a huge environmental impact of the raw materials and energy that goes into building and training AI systems. AI fundamentally challenges our notions of privacy, the inviolability of our personal data, and our own autonomy. Increasingly intelligent software and machines are reshaping the global work landscape and have the potential to displace human workers dramatically – despite increasing productivity.

The growing field of Ethics for AI and Responsible AI is working to understand, predict, and reduce the harm and risks caused by AI systems. Responsible AI teams combine research, ethics, technology, and business transformation to create the frameworks, expertise, and missing technologies that help companies reduce AI risks and improve ROI from this transformative technology.

Legal, financial and advisory firms are increasingly involved in AI risks.

Legal teams build cases where AI has been harmed, advise their clients on reducing AI risks, and navigate evolving global regulations around AI. In the financial sector, AI risks become brand risk and portfolio risk.

In response to the growing needs of professional teams to overcome AI risks, the AI ​​Responsibility Lab has developed the AI ​​Insights platform.

“When we talk to the legal teams, there is a desire to learn more about AI and help their clients here.” AIRL CEO Description Ramsay Brown. “But for many of them, it’s outside their core experience. It’s just continuing to emerge. What we’ve seen is that they’re missing the tools and community to build the understanding and inertia that they need here. And we saw that as an opportunity to help.”

AI Insights is the world’s most complete platform for AI risk intelligence and research.

AI Insights helps teams deliver valuable insights faster: sent straight to their inbox. Automated smart filters enable advanced logical combinations of searchable tags: for companies, risk factors (eg: racial bias, job displacement, data privacy), and geographic regions. This helps busy teams separate the signal from the noise and get only the exact alerts they care about.

Collect thousands of data to monitor the evolving AI risk landscape; Unifying them in one private dashboard, database and automated notification system. As a facet of the growing legal tech landscape, AI Insights puts breaking news and updates in the vocabulary of AI risks: every item is flagged, ranked, and prepared with powerful expert analysis. New risk incidents are recorded daily.

AI Insights is the latest release from the AI ​​Responsibility Lab, an extension of their flagship platform for Responsible AI Transformation, Task Control. Mission Control is a SaaS platform to accelerate the responsible transformation of AI. For teams in the exploratory stages of responsible AI maturity, Task Control improves learning and engagement and fosters a responsible AI culture. For advanced teams, task control enables token-free governance automation. Now companies can bridge the inevitable gaps between AI governance audits and the AI ​​business itself: through data integration, inference engines, and intelligent operating logic in an event-driven environment. Task control helps teams resolve fundamental deficiencies in coordination and visibility that lead to waste and harm and operationalize their aspirations in AI governance.

There is a great deal of work to get this right, as the world’s most successful companies realize the role that artificial intelligence plays in their success; A 2022 survey by Accenture of hundreds of global C-Suite executives showed a strong desire for the responsible transformation of AI: 69% of respondents indicated that their companies already had some level of AI governance, and more than 80% of respondents said they They will commit 10% or more of their total AI budget to meet regulatory requirements by 2024. In addition to the quality engineering benefits afforded by AI governance, leaders see the impending wave of global AI regulations as a strong incentive to take a proactive stance.

“Given the current pace of AI development, we know it is only accelerating. And as it stands, its impact on our entire lives cannot be underestimated. Helping legal and professional teams build the competencies and workflows they do The need to keep AI safe must be a priority,” Brown said. To be part of a better world that we need to build together.”

EasyList has been hit by a flood of very unusual traffic which is providing a problem for the community run project.

a Mail (Opens in a new tab) from AdGuard, and Ad blocker which uses its own filter list, claimed that since EasyList is used by 99.9% of all ad blockers, most users of these tools will also be affected.

a This is a comprehensive list of pop-up ads, trackers, and the like that ad blockers use to decide what to block and what to filter. An EasyList variant is a simple text file hosted on its own domain that an ad blocker periodically grabs for updates. However, it allegedly takes more than five minutes to download, due to a 10-20-fold increase in daily traffic that started earlier this month.

hit again

AdGuard mentioned in its post that it had the same problem last year, and assumed it stems from some browsing apps on Android devices in India. An apparent design flaw in these apps meant that they were trying to access the AdGuard filter menu every time they started (it happens frequently on Android devices), even when running in the background.

AdGuard’s solution was to block traffic coming from this Browserbut they claim that they still release over 100 terabytes of denied pages per month.

No support for EasyList

AdGuard claims that the EasyList problem is worse, however, unlike AdGuard, it cannot get any support from its hosting site, cloudto block these requests. AdGuard believes that this is due to the fact that it does not have an Enterprise account, which is the highest subscription level available for the service.

When EasyList contacted CloudFlare support, the email response stated that increased traffic resulted in a distributed denial of service (DDoSMitigation, CloudFlare’s security protocol that the company claims intelligently filters large amounts of traffic and breaks it down into manageable chunks to prevent denial of service.

The email also mentioned that EasyList was already in violation of the Terms of Service (ToS), as the company does not allow text file requests. The company suggested that EasyList would have to move the file to another subdomain to make it available to its users.

The result is that EasyList is throttled and no ad blocker can access the filter list directly. AdGuard believes the only option is for EasyList to change its domain name, as disabled browser apps will continue to run DDoS as long as the original easylist.to list is still in use. However, he warns that this is not an easy process and will have an additional impact on the expected thousands of open source projects currently using EasyList.

way forward

AdGuard states that all filter lists are hosted on its own domain, so users should not be affected.

Users of other software may be fine as well, as they may have already switched to using an inverted domain from which EasyList can be accessed. However, AdGuard warns that faulty browsers may eventually start to opt out of these as well and repeat the problem again.

Previously, AdGuard had tried to contact the developers of a faulty browser to let them know about the problem, but claimed that the problem had already worsened after doing so – indicating that there may be more faulty browsers.

A random forest model (RF) has similar accuracy to a support vector machine (SVM) for distinguishing patients with obstructive sleep apnea (OSA), according to a study published online Oct.

Bo Pang, of the University of California, Los Angeles, and colleagues examined whether using faster and less complex machine learning models, including SVM and RF, with brain diffusion tensor imaging (DTI) data could distinguish OSA from healthy controls. Two DTI series were obtained from 59 patients with OSA and 96 controls using 3.0-Tesla. Magnetic Resonance photogrammetry scanner Diffusion maps from each series were averaged using DTI data and were reorganized, averaged, normalized to a common space and used to perform cross-validation for model training, selection, and OSA prediction.

The researchers found that the RF model exhibited a classification accuracy of 0.73 for OSA and controls and an area under the curve value (AUC) on the receiver operator curve of 0.85. Cross-validation showed a similar fitting of the RF model with the SVM of OSA and control data (precision, 0.77; AUC, 0.84).

“OSA scanning can be faster and less complicated using Brain diffusion tensor imaging Data and machine learning. Such use of neuroimaging data and machine learning It will allow early screening for sleep apnea and intervention that can eventually help restore brain tissue changes and function,” the authors wrote.

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