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Oracle joins up with Nvidia to boost its artificial intelligence capabilities – The National

US software company Oracle announced a multiyear partnership with Nvidia a global leader in artificial intelligence hardware and software that designs and manufactures graphics processing units (GPUs) for various industries to boost its cloud infrastructure.

Under the partnership announced in parallel with the opening of the Oracle Cloud World event in Las Vegas, Nevada Oracle will use tens of thousands of Nvidia's GPUs to accelerate the pace of computing and AI advancements in its cloud infrastructure.

Following the announcement, Oracles stock was trading slightly up at $67.03 at 5.40pm New York time, while Nvidia was trading up at $119.67 a share.

The Texas-based company intends to bring the full Nvidia computing stack including GPUs, systems and software to Oracle Cloud Infrastructure (OCI).

GPUs can process various tasks simultaneously, making them useful for machine learning, video editing and gaming applications.

Nvidia is a global leader in AI hardware and software. Reuters

OCI is adding tens of thousands more Nvidia GPUs including the A100 and upcoming H100 to its capacity, Oracle said in a statement.

About a month ago, the US restricted Nvidia from exporting its A100 and H100 chips, designed to speed up machine-learning tasks, to China and Russia.

Combined with OCIs AI cloud infrastructure, cluster networking and storage, this partnership provides enterprises a broad, easily accessible portfolio of options for AI training and deep learning inference at scale, Oracle said.

To drive long-term success in todays business environment, organisations need answers and insight faster than ever, the company's chief executive Safra Catz said.

Our expanded alliance with Nvidia will deliver the best of both companies expertise to help customers across industries from health care and manufacturing to telecommunications and financial services overcome the multitude of challenges they face.

The Oracle and Nvidia partnership comes as more companies integrate AI and machine-learning tools to streamline their operations and as AI models become more complex.

The companies did not disclose the financial details of the deal.

US technology company Oracle announced a series of new cloud-focused products at Oracle Cloud World on Tuesday. Reuters

Accelerated computing and AI are key to tackling rising costs in every aspect of operating businesses, California-based Nvidias founder and chief executive Jensen Huang said.

Enterprises are increasingly turning to cloud-first AI strategies that enable fast development and scalable deployment. Our partnership with Oracle will put Nvidia AI within easy reach for thousands of companies.

The global AI market is expected to grow at an annual rate of more than 38 per cent from 2022 to 2030, from $93.5 billion last year, Grand View Research reported.

AI will be the common theme in the top 10 technology trends in the next few years, and these are expected to quicken breakthroughs across key economic sectors and society, Alibaba Damo Academy the global research arm of Chinese company Alibaba Group said in a report.

Updated: October 19, 2022, 12:30 PM

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The Regulation of Artificial Intelligence in Canada and Abroad: Comparing the Proposed AIDA and EU AI Act – Fasken

Laws governing technology have historically focused on the regulation of information privacy and digital communications. However, governments and regulators around the globe have increasingly turned their attention to artificial intelligence (AI) systems. As the use of AI becomes more widespread and changes how business is done across industries, there are signs that existing declarations of principles and ethical frameworks for AI may soon be followed by binding legal frameworks. [1]

On June 16, 2022, the Canadian government tabled Bill C-27, the Digital Charter Implementation Act, 2022. Bill C-27 proposes to enact, among other things, the Artificial Intelligence and Data Act (AIDA). Although there have been previous efforts to regulate automated decision-making as part of federal privacy reform efforts, AIDA is Canadas first effort to regulate AI systems outside of privacy legislation. [2]

If passed, AIDA would regulate the design, development, and use of AI systems in the private sector in connection with interprovincial and international trade, with a focus on mitigating the risks of harm and bias in the use of high-impact AI systems. AIDA sets out positive requirements for AI systems as well as monetary penalties and new criminal offences on certain unlawful or fraudulent conduct in respect of AI systems.

Prior to AIDA, in April 2021, the European Commission presented a draft legal framework for regulating AI, the Artificial Intelligence Act (EU AI Act), which was one of the first attempts to comprehensively regulate AI. The EU AI Act sets out harmonized rules for the development, marketing, and use of AI and imposes risk-based requirements for AI systems and their operators, as well as prohibitions on certain harmful AI practices.

Broadly speaking, AIDA and the EU AI Act are both focused on mitigating the risks of bias and harms caused by AI in a manner that tries to be balanced with the need to allow technological innovation. In an effort to be future-proof and keep pace with advances in AI, both AIDA and the EU AI Act define artificial intelligence in a technology-neutral manner. However, AIDA relies on a more principles-based approach, while the EU AI Act is more prescriptive in classifying high-risk AI systems and harmful AI practices and controlling their development and deployment. Further, much of the substance and details of AIDA are left to be elaborated in future regulations, including the key definition of high risk AI systems to which most of AIDAs obligations attach.

The table below sets out some of the key similarities and differences between the current drafts of AIDA and the EU AI Act.

High-risk system means:

The EU AI Act does not apply to:

AIDA does not stipulate an outright ban on AI systems presenting an unacceptable level of risk.

It does, however, make it an offence to:

The EU AI Act prohibits certain AI practices and certain types of AI systems, including:

Persons who process anonymized data for use in AI systems must establish measures (in accordance with future regulations) with respect to:

High-risk systems that use data sets for training, validation and testing must be subject to appropriate data governance and management practices that address:

Data sets must:

Transparency. Persons responsible for high-impact systems must publish on a public website a plain-language description of the AI system which explains:

Transparency. AI systems which interact with individuals and pose transparency risks, such as those that incorporate emotion recognition systems or risks of impersonation or deception, are subject to additional transparency obligations.

Regardless of whether or not the system qualifies as high-risk, individuals must be notified that they are:

Persons responsible for AI systems must keep records (in accordance with future regulations) describing:

High-risk AI systems must:

Providers of high-risk AI systems must:

The Minister of Industry may designate an official to be the Artificial Intelligence and Data Commissioner, whose role is to assist in the administration and enforcement of AIDA. The Minister may delegate any of their powers or duties under AIDA to the Commissioner.

The Minister of Industry has the following powers:

The European Artificial Intelligence Board will assist the European Commission in providing guidance and overseeing the application of the EU AI Act. Each Member State will designate or establish a national supervisory authority.

The Commission has the authority to:

Persons who commit a violation of AIDA or its regulations may be subject to administrative monetary penalties, the details of which will be establish by future regulations. Administrative monetary penalties are intended to promote compliance with AIDA.

Contraventions to AIDAs governance and transparency requirements can result in fines:

Persons who commit more serious criminal offences (e.g., contravening the prohibitions noted above or obstructing or providing false or misleading information during an audit or investigation) may be liable to:

While both acts define AI systems relatively broadly, the definition provided in AIDA is narrower. AIDA only encapsulates technologies that process data autonomously or partly autonomously, whereas the EU AI Act does not stipulate any degree of autonomy. This distinction in AIDA is arguably a welcome divergence from the EU AI Act, which as currently drafted would appear to include even relatively innocuous technology, such as the use of a statistical formula to produce an output. That said, there are indications that the EU AI Acts current definition may be modified before its final version is published, and that it will likely be accompanied by regulatory guidance for further clarity. [4]

Both acts are focused on avoiding harm, a concept they define similarly. The EU AI Act is, however, slightly broader in scope as it considers serious disruptions to critical infrastructure a harm, whereas AIDA is solely concerned with harm suffered by individuals.

Under AIDA, high-impact systems will be defined in future regulations, so it is not yet possible to compare AIDAs definition of high-impact systems to the EU AI Acts definition of high-risk systems. The EU AI Act identifies two categories of high-risk systems. The first category is AI systems intended to be used as safety components of products, or as products themselves. The second category is AI systems listed in an annex to the act and which present a risk to the health, safety, or fundamental rights of individuals. It remains to be seen how Canada would define high-impact systems, but the EU AI Act provides an indication of the direction the federal government could take.

Similarly, AIDA also defers to future regulations with respect to risk assessments, while the proposed EU AI Act sets out a graduated approach to risk in the body of the act. Under the EU AI Act, systems presenting an unacceptable level of risk are banned outright. In particular, the EU AI Act explicitly bans manipulative or exploitive systems that can cause harm, real-time biometric identification systems used in public spaces by law enforcement, and all forms of social scoring. AI systems presenting low or minimal risk are largely exempt from regulations, except for transparency requirements.

AIDA only imposes transparency requirements on high-impact AI systems, and does not stipulate an outright ban on AI systems presenting an unacceptable level of risk. It does, however, empower the Minister of Industry to order that a high-impact system presenting a serious risk of imminent harm cease being used.

AIDAs application is limited by the constraints of the federal governments jurisdiction. AIDA broadly applies to actors throughout the AI supply chain from design to delivery, but only as their activities relate to international or interprovincial trade and commerce. AIDA does not expressly apply to intra-provincial development and use of AI systems. Government institutions (as defined under the Privacy Act) are excluded from AIDAs scope, as are products, services, and activities that are under the direction or control of specified federal security agencies.

The EU AI Act specifically applies to providers (although this may be interpreted broadly) and users of AI systems, including government institutions but excluding where AI systems are exclusively developed for military purposes. The EU AI Act also expressly applies to providers and users of AI systems insofar as the output produced by those systems is used in the EU.

AIDA is largely silent on requirements with respect to data governance. In its current form, it only imposes requirements on the use of anonymized data in AI systems, most of which will be elaborated in future regulations. AIDAs data governance requirements will apply to anonymized data used in the design, development, or use of any AI system, whereas the EU AI Acts data governance requirements will apply only to high-impact systems.

The EU AI Act sets the bar very high for data governance. It requires that training, validation, and testing datasets be free of errors and complete. In response to criticisms of this standard for being too strict, the European Parliament has introduced an amendment to the act that proposes to make error-free and complete datasets an overall objective to the extent possible, rather than a precise requirement.

While AIDA and the EU AI Act both set out requirements with respect to assessment, monitoring, transparency, and data governance, the EU AI Act imposes a much heavier burden on those responsible for high-risk AI systems. For instance, under AIDA, persons responsible for such systems will be required to implement mitigation, monitoring, and transparency measures. The EU AI Act goes a step further by putting high-risk AI systems through a certification scheme, which requires that the responsible entity conduct a conformity assessment and draw up a declaration of conformity before the system is put into use.

Both acts impose record-keeping requirements. Again, the EU AI Act is more prescriptive, but contrary to AIDA, its requirements will only apply to high-risk systems, whereas AIDAs record-keeping requirements would apply to all AI systems.

Finally, both acts contain notification requirements that are limited to high-impact (AIDA) and high-risk (EU AI Act) systems. AIDA imposes a slightly heavier burden, requiring notification for all uses that are likely to result in material harm. The EU AI Act only requires notification if a serious incident or malfunction has occurred.

Both AIDA and the EU AI Act provide for the creation of a new monitoring authority to assist with administration and enforcement. The powers attributed to these entities under both acts are similar.

Both acts contemplate significant penalties for violations of their provisions. AIDAs penalties for more serious offences up to $25 million CAD or 5% of the offenders gross global revenues from the preceding financial year are significantly greater than those found in Quebecs newly revised privacy law and the EUs General Data Protection Regulation (GDPR). The EU AI Acts most severe penalty is higher than both the GDPR and AIDAs most severe penalty: up to 30 million or 6% of gross global revenues from the preceding financial year for non-compliance with prohibited AI practices or the quality requirements set out for high-risk AI systems.

In contrast to the EU AI Act, AIDA also introduces new criminal offences for the most serious offences committed under the act.

Finally, the EU AI Act would also grant discretionary power to Member States to determine additional penalties for infringements of the act.

While both AIDA and the EU AI Act have broad similarities, it is impossible to predict with certainty how similar they could eventually be, given that so much of AIDA would be elaborated in future regulations. Further, at the time of writing, Bill C-27 has only completed first reading, and is likely to be subject to amendments as it makes its way through Parliament.

It is still unclear how much influence the EU AI Act will have on AI regulations globally, including in Canada. Regulators in both Canada and the EU may aim for a certain degree of consistency. Indeed, many have likened the EU AI Act to the GDPR, in that it may set global standards for AI regulation just as the GDPR did for privacy law.

Regardless of the fates of AIDA and the EU AI Act, organizations should start considering how they plan to address a future wave of AI regulation.

For more information on the potential implications of the new Bill C-27, Digital Charter Implementation Act, 2022, please see our bulletin,The Canadian Government Undertakes a Second Effort at Comprehensive Reform to Federal Privacy Law, on this topic.

[1]There have been a number of recent developments in AI regulation, including the United Kingdoms Algorithmic Transparency Standard, Chinas draft regulations on algorithmic recommendation systems in online services, the United States Algorithmic Accountability Act of 2022, and the collaborative effort between Health Canada, the FDA and the United Kingdoms Medicines and Healthcare Products Regulatory Agency to publish Guiding Principles on Good Machine Learning Practice for Medical Device Development.

[2]In the public sphere, the Directive on Automated Decision-Makingguides the federal governments use of automated decision systems.

[3]This prohibition is subject to three exhaustively listed and narrowly defined exceptions where the use of such AI systems is strictly necessary to achieve a substantial public interest, the importance of which outweighs the risks: (1) the search for potential victims of crime, including missing children; (2) certain threats to the life or physical safety of individuals or a terrorist attack; and (3) the detection, localization, identification or prosecution of perpetrators or suspects of certain particularly reprehensible criminal offences.

[4]As an indication of potential changes, the Slovenian Presidency of the Council of the European Union tabled a proposed amendment to the act in November 2021 that would effectively narrow the scope of the regulation to machine learning.

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Gradient AI and Duck Creek Technologies Deliver Integrated Workers’ Compensation Underwriting and Claims Management Solutions, Leveraging Artificial…

LAS VEGAS--(BUSINESS WIRE)--Gradient AI, a leading enterprise software provider of artificial intelligence (AI) solutions in the insurance industry, and Duck Creek Technologies, a leading technology solutions provider to the property and casualty (P&C) insurance industry, today announced integrated AI workers compensation solutions and their first joint customer, Builders Mutual Insurance.

The joint offerings bring Gradient AI's state-of-the art AI solutions to insurance carriers that leverage Duck Creeks platform for their operations. This powerful combination allows underwriters and claims adjusters to uncover and analyze key drivers of workers compensation policy and claim risks, better assess them, and minimize their claims exposure, within the platform they already use.

Builders Mutual Insurance, a leading insurer for the construction industry in the Southeast, has adopted the joint solution to streamline claims, better identify risks and save training time. As adjusters enter their notes into the platform, the AI model learns and becomes even more accurate.

Builders Mutual is also using the solution to address one of the most pressing issues in the insurance industry today, the rising talent shortage. During the next 15 years, 50% of the current insurance workforce will retire; leaving more than 400,000 open positions unfilled, according to the U.S. Bureau of Labor Statistics. It will be challenging to replace these insurance workers leaving a significant talent gap. According to an Aon and Jacobson Group recent study, 53% of P&C insurance companies plan to aggressively hire within the next 12 months.

To address this challenge in its own operations, Builders is using the joint Duck Creek and Gradient AI solution to leverage the knowledge of its most seasoned people and convert it to institutional knowledge. Builders Mutuals agents now have access to the AI model enhanced by the unstructured data from experienced agents notes over many years. This knowledge base allows agents to leverage the experience of a seasoned adjuster that would otherwise have been lost.

Our organization continually looks for ways to give adjusters the tools to help them identify risks and best serve injured workers so they can recover quickly and return to work as soon as possible, said Ken Bunn, vice president of Claims, Builders Mutual Insurance. The integration of Duck Creek and Gradient AI helps with both of those goals, allowing adjusters to work more efficiently and effectively. It also saves us significant time when training new adjusters and helps keep our quality of service consistent as seasoned adjusters retire.

Bunn added, Previously, new adjusters would have to sit beside an experienced adjuster for years to observe how they were handling claims. Now, thanks to this integrated solution, our newest adjusters can learn quickly and have guardrails in place as they are making decisions.

These integrated solutions are delivering a truly reimagined experience for workers compensation underwriting and claims management, said Rohit Bedi, chief revenue officer of Duck Creek. Builders Mutuals adjusters now gain insights from AI solutions that are fully integrated into our Duck Creek platform, which is already a part of their normal workflow. They are getting these insights where they can have the greatest impact, at the point of decision.

The integration of Gradient AIs technology and Duck Creeks platform empower workers compensation underwriting and claims teams to process and prioritize workplace injuries more effectively and efficiently, said Stan Smith, CEO of Gradient. Builders Mutual is an innovator leveraging technology to deliver a better customer experience and achieve a better return on risk."

About Duck Creek Technologies

Duck Creek Technologies (NASDAQ: DCT) is the intelligent solutions provider defining the future of the property and casualty (P&C) and general insurance industry. We are the platform upon which modern insurance systems are built, enabling the industry to capitalize on the power of the cloud to run agile, intelligent, and evergreen operations. Authenticity, purpose, and transparency are core to Duck Creek, and we believe insurance should be there for individuals and businesses when, where, and how they need it most. Our market-leading solutions are available on a standalone basis or as a full suite, and all are available via Duck Creek OnDemand. Visit http://www.duckcreek.com to learn more. Follow Duck Creek on our social channels for the latest information LinkedIn and Twitter.

About Gradient AI

Gradient AI is a leading provider of proven artificial intelligence (AI) solutions for the insurance industry. Its solutions improve loss ratios and profitability by predicting underwriting and claim risks with greater accuracy, as well as reducing quote turnaround times and claim expenses through intelligent automation. Unlike other solutions that use a limited claims and underwriting dataset, Gradient's software-as-a-service (SaaS) platform leverages a vast dataset comprised of tens of millions of policies and claims. It also incorporates numerous other features including economic, health, geographic and demographic information. Customers include some of the most recognized insurance carriers, MGAs, TPAs, risk pools, PEOs and large self-insureds across all major lines of insurance. By using Gradient AIs solutions, insurers of all types achieve a better return on risk. To learn more about Gradient, please visit: https://www.gradientai.com/.

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How AI and VSaaS are Improving Safety in the Construction Sector – Spiceworks News and Insights

Video surveillance plays a vital role in the construction sector, and the rise of cloud video analytics, AI technology, and video surveillance as a service (VSaaS) offerings has the potential to take this to the next level. In this article, Logan Bell, head of product for Cloudview, shares the uses of AI and VSaaS and other benefits in the construction sector and how they are combined to improve the safety of the construction sector.

Health and safety are critical components of any business, but those in the construction sector need to take additional steps to look out for the safety of workers and protect their own construction firm from the potential financial, legal and reputational consequences associated with failures or shortcomings, in this area.

Video surveillance can play a vital role here, and the rise of cloud video analytics, artificial intelligence technology, and video surveillance as a service (VSaaS) offerings has the potential to take this to the next level. Lets look at precisely how AI and VSaaS are combined to improve safety within the construction sector.

First, it is essential to define precisely what is meant by artificial intelligence and video surveillance as a service. The former refers to technology that allows computers to perform complex actions, which have traditionally relied upon human intelligence, and is especially useful for automation and analysis of data.

VSaaS, on the other hand, refers to cloud-based surveillance services offered by third-party service providers. With a cloud-based video management system, users can remotely access the data captured by their surveillance system. Data is also stored in the cloud rather than on-site, allowing more footage to be stored while improving accessibility.

The two technologies can combine to provide cloud video analytics offerings. This will feed data from surveillance cameras through a layer of AI, pattern recognition, machine learning, and similar technologies to extract meaning from it and alert users to any actions, scenes, or situations deemed worthy of attention. As explained in an article from SDM, cloud deployment allows more processing power to be used for analytics purposes.

See More: Is Your Organization Ready to Secure Your Cloud Operations?

While a remote video surveillance system with cloud analytics capabilities can be helpful across many industries, the technology is especially valuable within construction, where there is such a strong emphasis placed on health and safety. In particular, AI and VSaaS can be used to great effect in the following areas:

One of the biggest ways in which AI and VSaaS are improving safety within the construction sector is by helping to keep construction sites secure. As a post for IIoT World explains, video analytics can be used to detect line crossing, loitering, increases in capacity, objects being taken, and a variety of other unwanted behaviors.

In situations where a construction site needs to be left unattended, a remote video surveillance system running AI analytics can detect intruders, alert site managers to items being taken, and continually monitor the construction site for other activities with no need for rest, allowing for better protection and much faster responses.

Video analytics also has the potential to identify individuals through facial recognition, and this can be used to manage access to the construction site. Regardless of whether it is during working hours or outside of them, keeping the site secure from unwanted intruders can prevent vandalism and keep workers safe.

While it is important to keep construction sites safe from intruders, it is also essential that steps are taken to minimize unwanted behaviors from construction workers too. A cloud-based video management system with AI-powered analytics can be trained to detect the presence of hard hats and flag situations where workers are not wearing them.

Beyond this, video analytics can be used to detect risky behaviors during the construction process itself. This can highlight dangerous acts or mistakes that might end up costly in the long term, but it can also help to prevent issues where the actual quality of the construction work may put people in jeopardy.

In the past, surveillance footage has been viewed after the fact. Yet, the rise of VSaaS and video analytics technology means that risky behavior can be detected in real-time, and site managers can take swift or even preventative action.

Modern VSaaS offerings still provide the conventional benefits of a surveillance system, such as the deterrent effect and the ability to provide evidence in the event of theft or vandalism, but these newer systems also provide some additional noteworthy benefits. For instance, actually identifying perpetrators becomes easier because cloud storage expands data limits, meaning footage can be recorded in 4K quality using multiple surveillance cameras.

In the event that an accident happens on a construction site, facial recognition technology can help to provide a better understanding of precisely what happened, who was involved, and what the response was.

As an article from Health & Safety Matters highlights, it is also common for construction workers to attend work while injured or experiencing ill health, and this is a growing problem that can have severe long-term consequences. Analytics can help to detect the presence of injuries or illnesses so that workers can be managed appropriately.

Construction workers face serious risks in their day-to-day lives, and firms employing these workers must take the right steps to keep them as safe as possible. Fortunately, VSaaS, AI, and cloud technology are helping to modernize on-site surveillance, allowing for real-time responses to unwanted behaviors and significant events.

How are you using AI and VSaaS to enhance your stance on safety and security? Share with us on Facebook, Twitter, and LinkedIn.

Image Source: Shutterstock

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How Can Artificial Intelligence Help With Suicidal Ideation? – Theravive

A new study published in the Journal of Psychiatric Research looked at the performance of machine learning models in predicting suicidal ideation, attempts, and deaths.

My study sought to quantify the ability of existing machine learning models to predict future suicide-related events, study author Karen Kusuma told us. While there are other research studies examining a similar question, my study is the first to use clinically relevant and statistically appropriate performance measures for the machine learning studies.

The utility of artificial intelligence has been a controversial topic in psychiatry, and medicine overall. Some studies have demonstrated better performance with machine learning methods, while others have not. Kusuma began the study expecting that machine learning models would perform well.

Suicide is a leading cause of years of life lost across most of Europe, central Asia, southern Latin America, and Australia (Naghavi, 2019; Australian Bureau of Statistics, 2020), Kusuma told us. Standard clinical practice dictates that people seeking help for suicide-related issues need to be first administered with a suicide risk assessment. However, research has found that suicide risk predictions tend to be inaccurate.

Only five per cent of people ordinarily classified as high risk died by suicide, while around half of those who died by suicide would normally be categorised as low risk (Large, Ryan, Carter, & Kapur, 2017). Unfortunately, there has been no improvement in suicide prediction research in the last fifty years (Franklin et al., 2017).

Some researchers have claimed that machine learning will become an efficient and effective alternative to current suicide risk assessments (e.g. Fonseka et al., 2019), Kusuma told us, so I wanted to examine the potential of machine learning quantitatively, while evaluating the methodology currently used in the literature.

Researchers searched for relevant studies across four research databases and identified 56 relevant studies. From there, 54 models from 35 studies had sufficient data, and were included in the quantitative analyses.

We found that machine learning models achieved a very good overall performance according to clinical diagnostic standards, Kusuma told us. The models correctly predicted 66% of the people who would experience a suicide-related event (i.e. ideation, attempt, or death), and correctly predicted 87% of the people who would not experience a suicide-related event.

However, there was a high prevalence of risk of bias in the research, with many studies processing or analysing the data inappropriately. This isnt a finding specific to machine learning research, but a systemic issue caused largely by a publish-or-perish culture in academia.

I did expect machine learning models to do well, so I think this review establishes a good benchmark for future research, Kusuma told us. I do believe that this review shows the potential of machine learning to transform the future of suicide risk prediction. Automated suicide risk screening would be quicker and more consistent than current methods.

This could potentially identify many people at risk of suicide without them having to reach out proactively. However, researchers need to be careful to minimise data leakage, which would skew performance measures. Furthermore, many iterations of development and validation need to take place to ensure that the machine learning models can predict suicide risk in previously unseen populations.

Prior to deployment, researchers also need to ascertain if artificial intelligence would work in an equitable manner across people from different backgrounds, Kusuma told us. For example, a study has found their machine learning models performed better in predicting deaths by suicide in White patients, as opposed to Black and American Indian/ Alaskan Native patients (Coley et al., 2022).

That isnt to say that artificial intelligence is inherently discriminatory, Kusuma explained, but there is less data available for minorities, which often means lower performance in those populations. Its possible that models need to be developed and validated separately for people of different demographic characteristics.

Machine learning is an exciting innovation in suicide research, Kusuma told us. An improvement in suicide prediction abilities would mean that resources could be allocated to those who need them the most.

Categories: Depression , Stress , Suicide | Tags: suicide, depression, machine

Patricia Tomasi is a mom, maternal mental health advocate, journalist, and speaker. She writes regularly for the Huffington Post Canada,focusing primarily on maternal mental health after suffering from severe postpartum anxiety twice. You can find her Huffington Post biography here. Patricia is also a Patient Expert Advisor for the North American-based,Maternal Mental Health Research Collectiveand is the founder of the online peer support group -Facebook Postpartum Depression & Anxiety Support Group - with over 1500 members worldwide. Blog:www.patriciatomasiblog.wordpress.com Email:tomasi.patricia@gmail.com

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Meta Has Developed AI for Real-Time Translation of Hokkien – Gizmodo

Metas Hokkien translator is the first speech-to-speech translator, but the AI can only translate one sentence at a time. Screenshot: Meta

Meta is chugging along on their Universal Speech Translator, which hopes to train an artificial intelligence to translate hundreds of languages in real time. Today, the tech giant claims to have generated the first artificial intelligence to translate Hokkien, which is a language primarily spoken and not written.

Hokkien is a language that is spoken by approximately 49 million people in countries like China, Taiwan, Singapore, Malaysia, and the Phillippines. Typically, training an AI to understand human speechand in Metas case, translationresearchers will feed the computer a large dataset of written transcripts. But Meta says that Hokkien is once of nearly 3,500 languages that are primarily spoken, meaning Hokkien does not have a large enough dataset to train the artificial intelligence since the language does not have a unified writing system.

As such, Meta focused on a speech-to-speech approach, as explained in the companys press release. Without going into too much detail, Meta explained that the input speech was translated into a sequence of acoustic sounds, which was then used to create waveforms of the language. Those waveforms were then coupled with Mandarin, which Meta identifies as a related language.

Meta says that the Hokkien translator is still a work in progress as the artificial intelligence can only translate one sentence at a time, but is being released as open-source so other researchers can build upon its work. The company is also releasing SpeechMatrix which is a large collection of speech-to-speech translations developed through our innovative natural language processing toolkit.

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Metas efforts at building tech to understand human language has a bit of a wonky past. The company released BlenderBot 3 earlier this year to show their attempt at creating an artificial intelligence chatbot. A previous investigation by Gizmodo found that the bots favorite movie was Mean Girls and that it really wanted you to know that racism is bad.

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What is the Impact of Artificial Intelligence on the Real Estate Industry – RealtyBizNews

The real estate industry is one of the many industries being disrupted by artificial intelligence (AI). From chatbots to predictive analytics, AI is changing the way real estate agents do business. Here's a look at how AI is impacting the real estate industry and what agents need to do to stay ahead of the curve.

Artificial intelligence sounds futuristic, no doubt influenced by popular culture. Yet true AI, the kind we have now, isnt nearly as advanced as to be able to do any of the things you see R2-D2 do in the latest Star Wars movie. Instead, todays AI is all about using advanced machine learning algorithms to process massive amounts of data as quickly as possible, identifying trends that could otherwise go unnoticed.

Because AI makes use of massive amounts of data its not just good at identifying trends its also adept at predicting future ones based on the information its provided. In fact, the larger the data set, the better modern AI performs with enough information at its disposal, a machine learning algorithm can provide incredible insights.

The real estate industry is, of course, the business of buying, selling, and investing in property. Real estate agents, brokers, investors, house flippers, construction companies, and property maintenance companies all fall under the real estate umbrella, either directly or tangentially.

Because there are so many different facets of the real estate industry, AI can be leveraged in dozens of different ways. A few examples include how real estate agents can use AI-assisted tools to build client relationships or how investors can use predictive analysis to help make determinations on the profitability of a property. The possibilities might not be limitless, but theyre highly promising.

So what, exactly, are the different ways that AI can impact the real estate industry for the better? Lets take a closer look below at four of the primary ways.

The more automation you can build into your processes, the better but this automation must be reliable. Otherwise, its not going to help you if its only going to deliver inaccurate or inconsistent results. This means that automating certain processes, especially those in client relationship management, needs to be done in ways that actually helps you build those relationships more efficiently.

The rise of so-called virtual assistants is emblematic of what AI can do for real estate professionals. A virtual assistant can automate tasks such as sending emails to your marketing list on a regular basis, for example. Other good ways to use AI-enabled assistance include an automated calculation tool that buyers, sellers, and agents alike can use, with the data the tool relies on being pulled from a large machine learning database.

Identifying trends and then predicting the impact that those new trends will have on the real estate market is a major selling point for artificial intelligence. Machine learning and analysis can parse massive amounts of data in a fraction of the time that people can, identifying both issues and opportunities before they arise.

AI-enabled predictive analytics can even be leveraged to create opportunities for greener real estate. HVAC systems, for example, make up 45 percent of energy usage in commercial buildings. 30 percent of that energy is wasted, but machine learning can be used to identify where waste is happening. This allows more targeted approaches to minimizing that waste, which reduces maintenance costs incurred by real estate investors.

A step up from a simple menu on a website, a chatbot is an interactive tool that allows site users to ask questions in real English and get accurate responses back. Programming a chatbot through traditional methods requires massive investments in time and resources, and the results are less than stellar its obvious to anyone using a traditional chatbot that were a far way off from useful interactivity.

Yet artificial intelligence increases the use of chatbots to near-human levels of interaction. Using machine learning, chatbots have access to massive amounts of data on the kinds of questions people tend to ask, allowing it to find better answers that are more helpful and less like talking to a robot. Using a chatbot in this way is hugely advantageous for real estate professionals looking to streamline their sales and marketing funnels effectively.

If the coronavirus pandemic has taught us anything, its that the ability to continue to live our lives during such times is dependent on strong remote business tools. Virtual reality and augmented reality are part of that, as you can leverage VR/AR tools to conduct business remotely and efficiently and the processing power of artificial intelligence helps make these capabilities a possibility.

In the world of real estate, being able to show properties virtually during the pandemic was a major boon. While nothing will replace truly walking through a property, having an interactive VR experience from across town or across the country where someone can explore the interior of a property without having to set foot inside it kept the real estate industry afloat during those tumultuous times.

New technologies always have the potential to disrupt the status quo. Embracing these new technologies from the outset is often the one factor that dictates long-term success, and this concept applies to the real estate industry just as it does to any other. AI and machine learning is just another step in the right direction for any real estate professional who wishes to see their business thrive.

Ben Shepardson is a Realty Biz News Contributing Writer and has a long track record of success in online marketing and web development. While pursuing a bachelors degree in Computer Information Systems, he worked doing enterprise-level SEO and started an online business offering web development services to small business customers.

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Artificial Intelligence in Fintech Market Research Report by Components, Application, Deployment, Region – Global Forecast to 2027 – Cumulative Impact…

New York, Oct. 17, 2022 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Artificial Intelligence in Fintech Market Research Report by Components, Application, Deployment, Region - Global Forecast to 2027 - Cumulative Impact of COVID-19" - https://www.reportlinker.com/p06336246/?utm_source=GNW 32% to reach USD 75.40 billion by 2027.

Market Statistics:The report provides market sizing and forecast across 7 major currencies - USD, EUR, JPY, GBP, AUD, CAD, and CHF. It helps organization leaders make better decisions when currency exchange data is readily available. In this report, the years 2018 and 2020 are considered as historical years, 2021 as the base year, 2022 as the estimated year, and years from 2023 to 2027 are considered as the forecast period.

Market Segmentation & Coverage:This research report categorizes the Artificial Intelligence in Fintech to forecast the revenues and analyze the trends in each of the following sub-markets:

Based on Components, the market was studied across Services and Solutions. The Services is further studied across Managed and Professional.

Based on Application, the market was studied across Business Analytics & Reporting, Customer Behavioral Analytics, Fraud Detection, Quantitative & Asset Management, and Virtual Assistant.

Based on Deployment, the market was studied across Cloud and On-Premise.

Based on Region, the market was studied across Americas, Asia-Pacific, and Europe, Middle East & Africa. The Americas is further studied across Argentina, Brazil, Canada, Mexico, and United States. The United States is further studied across California, Florida, Illinois, New York, Ohio, Pennsylvania, and Texas. The Asia-Pacific is further studied across Australia, China, India, Indonesia, Japan, Malaysia, Philippines, Singapore, South Korea, Taiwan, Thailand, and Vietnam. The Europe, Middle East & Africa is further studied across Denmark, Egypt, Finland, France, Germany, Israel, Italy, Netherlands, Nigeria, Norway, Poland, Qatar, Russia, Saudi Arabia, South Africa, Spain, Sweden, Switzerland, Turkey, United Arab Emirates, and United Kingdom.

Cumulative Impact of COVID-19:COVID-19 is an incomparable global public health emergency that has affected almost every industry, and the long-term effects are projected to impact the industry growth during the forecast period. Our ongoing research amplifies our research framework to ensure the inclusion of underlying COVID-19 issues and potential paths forward. The report delivers insights on COVID-19 considering the changes in consumer behavior and demand, purchasing patterns, re-routing of the supply chain, dynamics of current market forces, and the significant interventions of governments. The updated study provides insights, analysis, estimations, and forecasts, considering the COVID-19 impact on the market.

Cumulative Impact of 2022 Russia Ukraine Conflict:We continuously monitor and update reports on political and economic uncertainty due to the Russian invasion of Ukraine. Negative impacts are significantly foreseen globally, especially across Eastern Europe, European Union, Eastern & Central Asia, and the United States. This contention has severely affected lives and livelihoods and represents far-reaching disruptions in trade dynamics. The potential effects of ongoing war and uncertainty in Eastern Europe are expected to have an adverse impact on the world economy, with especially long-term harsh effects on Russia.This report uncovers the impact of demand & supply, pricing variants, strategic uptake of vendors, and recommendations for Artificial Intelligence in Fintech market considering the current update on the conflict and its global response.

Competitive Strategic Window:The Competitive Strategic Window analyses the competitive landscape in terms of markets, applications, and geographies to help the vendor define an alignment or fit between their capabilities and opportunities for future growth prospects. It describes the optimal or favorable fit for the vendors to adopt successive merger and acquisition strategies, geography expansion, research & development, and new product introduction strategies to execute further business expansion and growth during a forecast period.

FPNV Positioning Matrix:The FPNV Positioning Matrix evaluates and categorizes the vendors in the Artificial Intelligence in Fintech Market based on Business Strategy (Business Growth, Industry Coverage, Financial Viability, and Channel Support) and Product Satisfaction (Value for Money, Ease of Use, Product Features, and Customer Support) that aids businesses in better decision making and understanding the competitive landscape.

Market Share Analysis:The Market Share Analysis offers the analysis of vendors considering their contribution to the overall market. It provides the idea of its revenue generation into the overall market compared to other vendors in the space. It provides insights into how vendors are performing in terms of revenue generation and customer base compared to others. Knowing market share offers an idea of the size and competitiveness of the vendors for the base year. It reveals the market characteristics in terms of accumulation, fragmentation, dominance, and amalgamation traits.

Competitive Scenario:The Competitive Scenario provides an outlook analysis of the various business growth strategies adopted by the vendors. The news covered in this section deliver valuable thoughts at the different stage while keeping up-to-date with the business and engage stakeholders in the economic debate. The competitive scenario represents press releases or news of the companies categorized into Merger & Acquisition, Agreement, Collaboration, & Partnership, New Product Launch & Enhancement, Investment & Funding, and Award, Recognition, & Expansion. All the news collected help vendor to understand the gaps in the marketplace and competitors strength and weakness thereby, providing insights to enhance product and service.

Company Usability Profiles:The report profoundly explores the recent significant developments by the leading vendors and innovation profiles in the Global Artificial Intelligence in Fintech Market, including Active Intelligence Pte Ltd., Affirm, Inc., Amazon Web Services Inc., Cape Analytics Inc., CognitiveScale Inc., ComplyAdvantage Company, Core Scientific, Inc., Felicis Ventures Company, Google LLC, HighRadius Corporation, Inbenta Holdings Inc., Intel Corporation, International Business Machines Corporation, IPsoft, Inc., Microsoft Corporation, MindBridge Analytics Inc., Nuance Communications, Inc., Numerai Company, Oracle Corporation, Salesforce, Inc., TABLEAU SOFTWARE, LLC, and Upstart Network, Inc..

The report provides insights on the following pointers:1. Market Penetration: Provides comprehensive information on the market offered by the key players2. Market Development: Provides in-depth information about lucrative emerging markets and analyze penetration across mature segments of the markets3. Market Diversification: Provides detailed information about new product launches, untapped geographies, recent developments, and investments4. Competitive Assessment & Intelligence: Provides an exhaustive assessment of market shares, strategies, products, certification, regulatory approvals, patent landscape, and manufacturing capabilities of the leading players5. Product Development & Innovation: Provides intelligent insights on future technologies, R&D activities, and breakthrough product developments

The report answers questions such as:1. What is the market size and forecast of the Global Artificial Intelligence in Fintech Market?2. What are the inhibiting factors and impact of COVID-19 shaping the Global Artificial Intelligence in Fintech Market during the forecast period?3. Which are the products/segments/applications/areas to invest in over the forecast period in the Global Artificial Intelligence in Fintech Market?4. What is the competitive strategic window for opportunities in the Global Artificial Intelligence in Fintech Market?5. What are the technology trends and regulatory frameworks in the Global Artificial Intelligence in Fintech Market?6. What is the market share of the leading vendors in the Global Artificial Intelligence in Fintech Market?7. What modes and strategic moves are considered suitable for entering the Global Artificial Intelligence in Fintech Market?Read the full report: https://www.reportlinker.com/p06336246/?utm_source=GNW

About ReportlinkerReportLinker is an award-winning market research solution. Reportlinker finds and organizes the latest industry data so you get all the market research you need - instantly, in one place.

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Top 5 trends to watch in cloud computing – TechRepublic

Image: Who is Danny/Adobe Stock

As cloud computing gains popularity, software service vendors have been snatching up the opportunity to win business by providing new and improved technological solutions. Efforts to manage aspects like security, latency and complexity of these cloud-based infrastructures have resulted in several noteworthy trends occurring within the cloud computing market.

Based on recent activity in the software world, it is safe to say that cloud computing is an industry that will not fizzle out any time soon. According to a recent report presented by ReportLinker, the global cloud computing market size is expected to reach $1.1 trillion by 2028, with a market growth of 15% CAGR during this forecast period. Factors influencing this rise include the digitalization of workloads following the COVID-19 pandemic and the advanced business services provided by cloud technology.

Read on to learn about some of the top cloud computing trends that we at TechRepublic expect to stick around.

As more users make the transition to the cloud, providers are developing more methods for promoting edge computing utilization. By enabling users to process their data at the edge of their network, these companies allow connections from virtually anywhere.

Edge computing can provide better security, efficiency and decreased costs for its users who would be storing their data locally. In addition, it can provide access to areas that usually lack connections and enables continual and autonomous operations to occur even when disconnected. For this reason, many organizations that require connections in extreme environments have chosen to adopt this technology.

SEE: Dont curb your enthusiasm: Trends and challenges in edge computing (TechRepublic)

Conducting computing at the edge of a network can also minimize network latency and reduce bandwidth demands. These are driving factors for the adoption of edge computing to support increasingly popular uses like virtual reality systems.

Cloud security systems and data loss prevention solutions help organizations secure their infrastructures and adhere to cybersecurity standards which can become difficult to manage otherwise. Conducting business operations on cloud or hybrid systems can put organizations at more risk of security breaches and cyber threats.

Operational compliance and reporting risks pose significant risks to organizations adopting cloud computing, as they can result in financial penalties, data breaches and damage to the companys reputation and customer trust. However, adopting cloud security solutions like governance policies and compliance automation can reduce the likelihood of these risks.

Fortunately, many vendors provide solutions for organizations that wish to benefit from the flexibility of cloud computing without putting their security at risk. These systems can offer features for data loss prevention like encryption, authentication, endpoint protection and access management.

Additionally, configuration or compliance vulnerability scans may also be conducted through these solutions to help users stay one step ahead of cybersecurity threats. For now, this trend is ongoing, with many vendors focusing on the goal of providing centralized security management solutions.

While some organizations have fully transitioned to cloud computing, others still operate on hybrid infrastructures. Some also appreciate the flexibility of multi-cloud infrastructures that offer access to on-premise technologies, private clouds and public clouds. Unfortunately, while these infrastructure models help facilitate digital transformation, they can also lead to increased complexity and costs for users.

SEE: Research: The complexities of multicloud deployments are often worth the benefits, even in uncertain times (TechRepublic Premium)

A growing trend regarding these infrastructures involves securing the larger potential attack surfaces for organizations utilizing these models. As more complexity leads to more vulnerabilities, vendors are designing solutions to defend these systems from cloud-related security threats.

Cloud application migration and development and diverse cloud data storage are other popular topics surrounding hybrid and multi-cloud infrastructures. For organizations that desire help with these processes, vendors are already offering services to assist. We can expect to see even more advancements in this space that can help reduce the complexity of these models, so users can utilize a hybrid workspace or enjoy public cloud capabilities while maintaining the greater control of a private cloud.

Yes, you read that right the normalization of AI, ML and even automation is a trend that will not likely go away anytime soon. These technologies enable features that consumers highly desire, and service suppliers are doing their all to meet these demands.

AI and automation have become commonplace in cloud technologies, and ML is catching up. ML algorithms can take business decision-making to the next level, enabling organizations to assess potential opportunities and forecast risk factors. In addition, data to train ML models supports solutions that are more precisely attuned to the organizations needs.

Cloud and back-end operations are likely to continue featuring AI for analytical capabilities and solutions that can react appropriately to outside influencers. For example, organizations use serverless cloud technology to automate IT management tasks. Quantum computing is another up-and-coming tech trend that allows for information to be processed at a high rate, leading to faster time to reach insights and perform automated actions.

Perhaps companies have realized that consumers appreciate supporting businesses that prioritize environmental protection, or maybe they genuinely care about the well-being of future generations. Either way, green computing initiatives have become a growing trend in the cloud computing scene, and service providers arent shying away from mentioning it in their marketing campaigns.

The push toward adopting environmentally friendly practices through cloud-based services mainly aims to support sustainable computing by reducing energy consumption, carbon emissions and physical storage. According to a recent report by MarketsandMarkets the green technology and sustainability market size is projected to grow from $11.2 billion in 2020 to $36.6 billion by 2025, at a compound annual growth rate of 26.6% during this forecast period.

While cloud computing is generally thought of as an environmentally beneficial option, companies within the industry have been upping their efforts toward greener initiatives and minimizing their carbon footprint. For example, AWS has been adopting more sustainable cloud technologies, such as more power-efficient hardware designs, airflow technology to decrease energy requirements for cooling systems and renewable energy usage within its data centers.

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Benefits of Cloud Computing That Can Help You With Your Business 2023 – ReadWrite

Today, businesses are looking to operate more flexibly and cost-effectively. This has led to the rise of cloud computing as a viable solution for almost every business. Cloud computing uses a network of remote servers hosted on the Internet and accessible through standard web browsers or mobile apps.

It enables users to store data remotely, exchange files, and access software applications from anywhere with an internet connection. In addition, individuals and businesses that use the cloud can access their data from any computer or device connected to the internet, allowing them to sync their settings and files wherever they go.

There are many advantages to using cloud services for your business. Here are 10 benefits of cloud computing that can help you with your business.

One of the most significant benefits of cloud computing is its security. If you run your business on the cloud, you dont have to worry about protecting your data from hackers or other threats.

Cloud providers use industry-standard security practices to keep your data safe, including firewalls, encryption, and authentication systems.

You can further customize your businesss security settings if your business uses a private cloud. For example, if an employee loses or misplaces a device that has access to your data, you can remotely disable that device without putting your data at risk.

You can also encrypt your data to protect it against cyber threats. Businesses can also use multi-factor authentication (MFA) to protect their data further. MFA requires users to input a one-time passcode sent to their phone to log in and confirm their identity.

Another advantage of cloud computing is its scalability. Cloud providers offer scalable cloud solutions that you can adjust to meet your businesss needs.

You can scale up or down your system on demand to deal with seasonal traffic or unexpected spikes in usage. This allows you to avoid buying too much computing power and resources upfront and allows your business to adjust to changes in demand quickly.

You can also try out a cloud solution before you commit to it by renting a smaller instance for a trial period. Cloud solutions are also flexible enough for you to upgrade or downgrade your solutions as your business scales up or down.

This means that you dont have to buy more computing power than you need upfront, and you dont have to upgrade your systems again if your business starts to slow down.

Cloudcomputing can help you achieve greater flexibility and mobility if your business relies on people working remotely. With cloud solutions, you can access your data and run your applications from any computer or device connected to the internet.

When you can access all your data from anywhere, employees can work from home, in coffee shops, or other locations without sacrificing productivity. In addition, cloud providers offer a wide range of collaboration and communication tools that work with their services.

You can also use these tools to collaborate and communicate with clients and vendors who dont need access to your companys data.

Another advantage of cloud computing is its consistency. While different people and departments may use other devices and software, cloud solutions ensure everyone has a consistent experience.

This prevents miscommunications and ensures that everyone is on the same page. Whether you use Office 365, Google G Suite, Salesforce, or another cloud service, your business will have a consistent experience across platforms.

You can also use tools, like identity integration, to access information from different applications without switching between them.

Cloud solutions offer significant cost reductions over the long run compared to other IT solutions. You can save money on hardware, upgrades, and software licenses while enjoying a flexible and scalable solution.

Cloud providers handle all the maintenance and upgrade of their systems, so you dont have to worry about keeping up with the latest trends in IT.

Cloud solutions offer significant cost reductions over the long run compared to other IT solutions. You can save money on hardware, upgrades, and software licenses while enjoying a flexible and scalable solution.

You can easily integrate multiple cloud services to streamline your workflows if your business uses various cloud services.

Many cloud services have a wide range of integrations with other services that you can use to enhance your business processes. For example, you can use Salesforce to manage your leads and close rates and Zapier to link it with other business tools like Gmail, Mailchimp, and Google Calendar.

You can also use a hybrid cloud solution that lets you keep your data close to home while accessing additional IT services through the cloud.

Cloud solutions offer unlimited storage, unlike other data storage solutions like on-premise computers. So while you can scale down your cloud solution if you dont need as much storage for your data, you can also increase your storage later.

You can also use a hybrid solution to keep some of your data local while storing other data in the cloud.

Another advantage of cloud computing is faster performance. In addition, if you use the cloud, you arent limited by your hardware, and your systems are more scalable.

This means that your website and other business applications will perform faster without you having to make hardware upgrades.

You can also use a hybrid solution to improve your performance by keeping your most critical data close to home while accessing other data in the cloud.

Cloud solutions offer a collaborative online environment that lets you share important information with clients and vendors. You can use collaboration tools like wikis, blogs, and forums to work with team members and manage your projects.

You can also use collaboration tools to communicate with clients and vendors who dont need access to your companys data. These tools let you share documents, collaborate on tasks, and manage your workflow from a single platform.

Even though control is an essential aspect of a companys success, there are certain things that you cannot control. Whether your organization controls its own procedures or not, there are certain things that are out of your control. In todays market, even a small amount of downtime has a significant impact.

Business downtime leads to lost productivity, revenue, and reputation. Although you cant prevent or foresee all the catastrophes, there is something you can do to speed up your recovery. Cloud-based data recovery services provide quick recovery for emergency situations, such as natural disasters and electrical outages.

The range of benefits of Cloud computingmakes it a viable solution for almost every business. It offers many advantages that can help you streamline your workflow, achieve better performance, and operate more efficiently.

Suvigya Saxena is the Founder & CEO of Exato Software, a global ranking mobile, cloud computing and web app development company. With 15+ years of experience of IT Tech which are now global leaders with creative solutions, he is differentiated by out-of-the-box IT solutions throughout the domain.

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