The U.S. Securities and Exchange Commission (SEC) has charged the team behind Coindeal, a $45 million fraudulent crypto investment scheme. The regulator explained that the defendants falsely claimed that Coindeal would generate investment returns of more than 500,000 times for investors.

The U.S. Securities and Exchange Commission (SEC) announced Wednesday that it has charged the creator of crypto investment scheme Coindeal and seven others in connection with the $45 million fraud.

Describing Coindeal as a brazen and far-reaching unregistered offering fraud conducted between at least 2018 and 2022, the securities regulator detailed:

Coindeal raised more than $45 million from sales of unregistered securities to tens of thousands of investors worldwide.

The SEC explained that creator Neil Chandran and promoters Garry Davidson, Michael Glaspie, Amy Mossel, and Linda Knott falsely claimed that investors could generate extravagant returns by investing in a blockchain technology called Coindeal that would be sold for trillions of dollars to a group of prominent and wealthy buyers.

However, the regulator said no Coindeal sale ever occurred and no distributions were made to investors. The defendants collectively misappropriated millions of dollars of investor funds for personal use, and Chandran used investor funds to purchase items such as cars, real estate, and a boat, the SEC wrote.

The securities watchdog also charged AEO Publishing Inc., Banner Co-Op Inc., and Bannersgo LLC for their involvement in the fraudulent crypto investment scheme.

Daniel Gregus, director of the SECs Chicago Regional Office, said:

We allege the defendants falsely claimed access to valuable blockchain technology and that the imminent sale of the technology would generate investment returns of more than 500,000 times for investors.

The director added: As alleged in our complaint, in reality, this was all just an elaborate scheme where the defendants enriched themselves while defrauding tens of thousands of retail investors.

In June last year, the U.S. Department of Justice (DOJ) indicted Chandran on three counts of wire fraud and two counts of monetary transaction in unlawful proceeds in connection with the Coindeal crypto fraud scheme.

What do you think about the SECs action against Coindeal? Let us know in the comments section below.

A student of Austrian Economics, Kevin found Bitcoin in 2011 and has been an evangelist ever since. His interests lie in Bitcoin security, open-source systems, network effects and the intersection between economics and cryptography.

Image Credits: Shutterstock, Pixabay, Wiki Commons

Disclaimer: This article is for informational purposes only. It is not a direct offer or solicitation of an offer to buy or sell, or a recommendation or endorsement of any products, services, or companies. Bitcoin.com does not provide investment, tax, legal, or accounting advice. Neither the company nor the author is responsible, directly or indirectly, for any damage or loss caused or alleged to be caused by or in connection with the use of or reliance on any content, goods or services mentioned in this article.

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LAS VEGAS, Jan. 06, 2023 (GLOBE NEWSWIRE) -- Connect Invest, an online investment company, increased all of the interest rates today on its short-note offerings following the recent Fed rate hike to provide investors with an opportunity to earn significant returns with short-term commitments and low investment minimums, starting at $500. Short notes allow investors to commit their money for short commitments with defined exit dates.

While interest rates have risen on savings and CD accounts, they continue to have a negative real yield as theU.S. inflation rate sits at 7.11%. According to theFederal Deposit Insurance Corp., the averagenational deposit rate for savings accounts is 0.3%, while the average for 24-month CD accounts is 1.06%.

Connect Invest offers an alternative investment vehicle that mitigates both market volatility and uncertainty while providing residual income through the form of monthly interest payments. "We view this uncertain time in the economy as an opportunity to offer unprecedented value to our clients," said Brandon Kelly, Vice President of Marketing and Operations at Connect Invest. "We look forward to serving new investors seeking stable residual monthly income during the term of their investment."

Previously, return rates for Connect Invest short notes six-month and 12-month commitments were 5.5% and 7.25%, respectively. The new rates are now 7.5% and 8%, respectively. The 24-month short note continues to pay 9%. Funds from all short note investments are used for purchasing first-position collateralized notes of various real estate projects. With access to over $500 million in collateralized projects, Connect Invest is constantly adding projects to the portfolio for continued diversification.

To learn more about Connect Invest, visit http://www.connectinvest.com/about-us/

About Connect Invest

Connect Invest is an alternative investment platform specializing in collateralized debt through real estate short-note investments. We offer short-term investments in real estate development projects that yield high returns monthly, with zero overhead and no account or maintenance fees. Our investments are determined based on the investor's risk tolerance, investment amount, and length of term. Available to both accredited and nonaccredited investors, all funds are used to fund a variety of real estate development projects throughout the country at various stages, including acquisition, development and construction. Investments start as low as $500, terms as short as 6 months, and interest earnings ranging from 7.5% to 9%.

Contact Information: Diana Calderon Marketing Director dcalderon@connectinvest.com

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Connect Invest, the industry's leading collateralized debt investment ... - GlobeNewswire

Jan. 5Could theLincoln Airportbecome a major cargo hub?

AColoradocompany is optimistic that it can, so much so that it plans to invest tens of millions of dollars to make that happen.

TheLincoln Airport Authorityon Thursday approved an agreement withBurrell AviationofAspen, Colorado, that gives the company the right to develop about 30 acres of property on the west side of the airport with the aim of transforming it into a hub for air cargo and other aviation-related industries.

The agreement includes a 30-year ground lease with options that could extend it to as long as 50 years. The lease allows Burrell to build on airport property at the south end of what's called the west ramp.

Burrell CEOJohn Carversaid the estimated $65 million investment, which does not include the cost of the ground lease with theAirport Authority, will occur over several years.

Carver said the company plans to focus on three industries as it seeks to boost the airport's nonaviation operations: air cargo handling, cold storage and distribution.

That could mean attracting air freight companies, logistics firms, food businesses and aircraft maintenance providers.

"I think we'll find the interest here to be pretty robust," Carver said.

Facilities would be built in phases, with two or three large buildings likely. Burrell is proposing up to 210,000 square feet of space. Carver said he believes the first tenants could occupy spaces as soon as 2025. At full build-out, he estimated the development could add anywhere from 180 to 350 permanent jobs with salaries averaging $60,000 to $80,000 a year.

The Lincoln Airportis one of about 20 Burrell is working with across the country, fromAlaskatoNew England. Most of them are in smaller cities like Lincoln.

The company already has announced plans for similar developments at airports inAlbuquerque, New Mexico;Baton Rouge, Louisiana; andColorado Springs, Colorado.

Carver said Burrell looked at the more than 5,000 commercial airports in theU.S.and used a filtering process to whittle it down to a few dozen it hoped to work with.

Among the things it looked for as it seeks to take advantage of the need to serve the growing e-commerce industry was proximity to large cities, a good highway network and an airport administration that has a forward-thinking approach.

Lincoln, Carver said, "checked all of those boxes."

"The Lincoln Airportis an ideal location to add to our portfolio," he said. "It provides a strategic presence in the heartland of America, with an airport that is accessible to other major transportation modes such as interstates and rail lines."

Lincoln Airport Executive DirectorDavid Haringsaid the agreement has nothing but upside for the airport. It gets rent from the ground lease and will eventually owns any structures Burrell builds once the lease term expires.

"This is kind of an easy deal for the airport," Haring said. "The heavy lifting is being done by Burrell."

"I'm really excited not just for the airport but for the community and all ofSoutheast Nebraska," he said.

Local and state leaders had nothing but praise for the deal.

"Landing this opportunity prepares Lincoln for yet another economic takeoff," MayorLeirion Gaylor Bairdsaid in a statement. "We're proud thatBurrell Aviationidentified our community as the location for the next project and look forward to supporting their exciting development at the Lincoln Airport.

NewNebraskaGov.Jim Pillen, who took office on Thursday, called the Burrell proposal "another example of business stepping up and investing in the future ofNebraska."

"I am excited about this opportunity and the growth it will bring to Lincoln," he said in a statement.

While the Lincoln Airport took a big hit during the coronavirus pandemic, losing more than half its passenger traffic and one of its two airlines, it has been making strides over the past couple of years, especially when it comes to improving its infrastructure.

The airport is in the midst of a $55 million terminal expansion and renovation project, the first phase of which it hopes to have done this spring. That phase will add gates, consolidate passenger screening and add new amenities.

The airport also, likely in the next two or three years, will start a project to rebuild its main runway, which is one of the longest at any commercial airport in theU.S.

Thanks to an agreement brokered by Sen.Deb Fischer, theNational Guard Bureauhas agreed to chip in to help pay to keep the runway at nearly 13,000 feet long, which is important because theFederal Aviation Administration, which covers 90% of the cost of most airport capital improvements, has said it likely wouldn't pay to rebuild a runway of that length.

The length of Lincoln's runway was apparently a factor that Burrell considered in whether to invest here, a point that Fischer highlighted in a statement.

"Thanks in part to my work to secure funding for the rehabilitation of the Lincoln Airport runway, one of the world's leading cargo carriers and logistics companies is now coming toNebraska," she said.

Reach the writer at 402-473-2647 ormolberding@journalstar.com.

On Twitter @LincolnBizBuzz.

___

(c)2023 Lincoln Journal Star, Neb.

Visit Lincoln Journal Star, Neb. at http://www.journalstar.com

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Hundreds of companies pay dividends to their investors. Because of that, it can be hard to know which one to pick if you're looking to keep your portfolio holdings manageable.

Onedividend stockthat stands out from the crowded field isOneok(OKE 2.68%). Thepipeline companyoffers a unique investment opportunity amongS&P 500members. Here's what makes it so distinct.

Oneok stands out among stocks in the S&P 500:

Image source: Oneok Investors Relations Presentation.

There are around 500 companies in the S&P 500. However, only 380 have investment-grade credit ratings. That means they have the financial strength to weather an economic downturn. This characteristic could be important in 2023 if there's a recession.

Meanwhile, only 269 of those companies have largemarket capsof over $20 billion. Larger companies are more mature and stable, making them good portfolio anchors.

From that group, only 176 have high environmental, social, and governance (ESG)ratings of A or better from MSCI. In Oneok's case, it's an ESG leader with a AAA rating thanks to its strong corporate governance, lower carbon emissions, and health and safety standards. While it might seem strange to see a fossil fuel-focused company with such high environmental ratings, Oneok has helped lead the industry's efforts to reduce natural gas flaring in the Williston Basin by capturing this gas. It also provides the infrastructure to support renewable natural gas, helping prevent those methane emissions. Finally, the company aims to reduce its emissions by 30% by 2030. Because of that, it's making the energy sector much more sustainable.

From that group of sustainable, large-cap, investment-grade-rated companies, only 135 are on pace to grow their earnings per share by a 5% or greater rate over the next couple of years.

Finally, Oneok is the only company of that remaining group with a high-yielding dividend that it has never reduced. That track record of dividend stability is impressive, considering that many energy stocks have reduced their payouts in the past due to the sector's volatility.

Oneok has done more than maintain its dividend over the years. The pipeline company has steadily increased it over time. While it hasn't grown its payout every year, it has expanded it at a 13% compound rate since 2000.

The company should have the fuel to continue growing its dividend in the future. Its earnings continue to rise as it benefits from the $5 billion of expansion projects it completed in the recent past, setting it up to grow volumes and benefit from favorable commodity prices. Its earnings per share have increased by 13% over the past year. Meanwhile, the company expects its income to grow by more than 10% in 2023. Those rising earnings will supply Oneok with more money to sustain its dividend.

Further, as noted, the company has an investment-grade balance sheet. Leverage was a comfortable 3.8 times at the end of the third quarter, giving Oneok the financial capacity to invest in new expansion projects as opportunities arise while also continuing to pay its attractive dividend. The company funded nearly $900 million of capital projects through the third quarter to maintain and expand its energy infrastructure network.

Meanwhile, Oneok continues to seek new growth drivers that could give it the fuel to keep growing. For example, the company recently filed for a permit to build the Saguaro Connector Pipeline that would transport gas to the Mexican border for delivery to an export facility on that country's west coast. The company hopes to make a final investment decision on the project by the middle of 2023. Oneok has an excellent track record of completing expansion projects that help sustain and grow its dividend.

Oneok stands out among dividend stocks. It offers investors a unique blend of safety, size, sustainability, growth, and income. That compelling blend of features makes it a high-quality dividend stock that could anchor any income portfolio.

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Healthcare's comfort level with artificial intelligence and machine learning models and skill at deploying them across myriad clinical, financial and operational use cases continued to increase in 2023.

More and more evidence shows that training AI algorithms on a variety of datasets can improve decision support, boost population health management, streamline administrative tasks, enable cost efficiencies and even improve outcomes.

But there's still a lot work to be done to ensure accurate, reliable, understandable and evidence-based results that ensure patient safety and account for health equity.

Theres no doubt that AIs application in healthcare has gone beyond "real in 2019 tosignificant investmentby providers and payers last year. This year, weve reported on deeper industry discussions focused on trust and best practices. Weve featured industry perspectives on the values ofdeep learning and neural networksand how to clear data hurdles along with announcements of successful studies and of course, new healthcare AI technology partnerships. Here are Healthcare IT News most-read AI stories of 2022.

How AI bias happens and how to eliminate it. Though posted about 30 days before the close of 2021, readers flocked to read the advice of Stanford cardiologist Dr. Sanjiv M. Narayan, co-director of the Stanford Arrhythmia Center, director of its Atrial Fibrillation Program and professor of medicine at Stanford University School of Medicine. Narayan discussed multiple approaches to eliminate bias in AI, including training multiple versions of algorithms, adding multiple datasets to AI and updating a machines training datasets over time. He cautioned that algorithmic hygiene strategies are not foolproof, and that bias is more likely to compound when integrating complex systems.

Developing trust in healthcare AI, step by step. While usage of AI in healthcare has increased, providers have been concerned about how much they should trust machine learning in clinical settings. A Chilmark Research report by analyst Dr. Jody Ranck indicated that, based on a review of hundreds of first-year COVID-19 pandemic algorithms, numerous instances of AI could not be validated. Ranck proposed strategies to increase evidence-based AI development.

Sentient AI? Convincing you its human is just part of LaMDAs job. In this guest post, which was published after amainstream mediafeeding frenzy about an ostensibly "sentient" machine learning application. Dr. Chirag Shah, associate professor at the Information School at the University of Washington, explains how Googles LaMDA chatbot, which easily passed the Turing Test, does not prove the presence of self-aware consciousness. LaMDA proves only that it can create the illusion of possessing self-awareness which is exactly what it was designed to do.

Duke, Mayo Clinic, others launch innovative AI collaboration. Artificial intelligence researchers and technology leaders from Duke, Mayo Clinic, University of California Berkeley and others unveiled a new Health AI Partnership at a virtual HIMSS learning event just before the close of 2021. By developing an online curriculum to help educate IT leaders and working with stakeholders, the collaborators are aiming to develop a standardized, evidence-driven process for AI deployments in healthcare.

The intersection of remote patient monitoring and AI. Robin Farmanfarmaian, author of "How AI Can Democratize Healthcare: The Rise in Digital Care" and four other books, discussed how AI is impacting remote patient monitoring today and how it can democratize healthcare. "RPM has the ability to collect clinical-grade data when people are in all stages of health and at all ages," she said."When collected continuously in machine-readable databases, once RPM is more fully adopted, those databases have the ability to dwarf EHR data from a hospital or health system."

Mayo launches AI startup program, with assists from Epic and Google. In March, The Mayo Clinic launched a 20-week startup program to give early-stage health tech AI companies a boost. The clinic's technology, medical and business experts, along with thought leaders from Google and Epic, were to provide the cohort with expertise to help the startups delineate AI model requirements.

AI study finds 50% of patient notes duplicated. University of Pennsylvania Perelman School of Medicine in Philadelphia researchers used natural language processing to find the rate of note duplication, as well as the rate of duplication year over year, across the records of 1.96 million unique patients from 2015 to 2020. "Duplicate text casts doubt on the veracity of all information in the medical record, making it difficult to find and verify information in day-to-day clinical work," according to their JAMA report published in September.

AWS, GE leaders talk hurdles to data-sharing, AI implementation. In a fireside chat at HIMSS22, Amazon Web Services' Dr. Taha Kass-Hout and GE Healthcare's Vignesh Shetty discussed the challenges of AI and the opportunities for making better-connected decisions.

How AI and machine learning can predict illness and boost health equity. In a recent Q&A, Brett Furst, president of HHS Tech Group, discussed how leveraging the COVID-19 Research Database one of the world's most comprehensive cross-linked data sets can establish cause-effect relationships between multiple variables. When machine learning determines how multiple variables interact, it can reliably predict health outcomes.

CommonSpirit Health gains huge efficiencies with AI-infused OR scheduling tool. This case study, featuring Brian Dawson, system vice president of perioperative services at CommonSpirit, showed how the health system implemented an AI utilization tool that would improve operating room efficiencies across its 350 hospitals. "Healthcare providers across the globe have had to do more with less, and it has led to increased burnout, staff shortages, patient dissatisfaction and scarce resources," said Dawson.

Andrea Fox is senior editor of Healthcare IT News.Email:afox@himss.orgHealthcare IT News is a HIMSS publication.

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Top 10 AI and machine learning stories of 2022 - Healthcare IT News

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Its as good a time as any to discuss the implications of advances in artificial intelligence (AI). 2022 saw interesting progress in deep learning, especially in generative models. However, as the capabilities of deep learning models increase, so does the confusion surrounding them.

On the one hand, advanced models such as ChatGPT and DALL-E are displaying fascinating results and the impression of thinking and reasoning. On the other hand, they often make errors that prove they lack some of the basic elements of intelligence that humans have.

The science community is divided on what to make of these advances. At one end of the spectrum, some scientists have gone as far as saying that sophisticated models are sentient and should be attributed personhood. Others have suggested that current deep learning approaches will lead to artificial general intelligence (AGI). Meanwhile, some scientists have studied the failures of current models and are pointing out that although useful, even the most advanced deep learning systems suffer from the same kind of failures that earlier models had.

It was against this background that the online AGI Debate #3 was held on Friday, hosted by Montreal AI president Vincent Boucher and AI researcher Gary Marcus. The conference, which featured talks by scientists from different backgrounds, discussed lessons from cognitive science and neuroscience, the path to commonsense reasoning in AI, and suggestions for architectures that can help take the next step in AI.

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Deep learning approaches can provide useful tools in many domains, said linguist and cognitive scientist Noam Chomsky. Some of these applications, such as automatic transcription and text autocomplete have become tools we rely on every day.

But beyond utility, what do we learn from these approaches about cognition, thinking, in particular language? Chomsky said. [Deep learning] systems make no distinction between possible and impossible languages. The more the systems are improved the deeper the failure becomes. They will do even better with impossible languages and other systems.

This flaw is evident in systems like ChatGPT, which can produce text that is grammatically correct and consistent but logically and factually flawed. Presenters at the conference provided numerous examples of such flaws, such as large language models not being able to sort sentences based on length, making grave errors on simple logical problems, and making false and inconsistent statements.

According to Chomsky, the current approaches for advancing deep learning systems, which rely on adding training data, creating larger models, and using clever programming, will only exacerbate the mistakes that these systems make.

In short, theyre telling us nothing about language and thought, about cognition generally, or about what it is to be human or any other flights of fantasy in contemporary discussion, Chomsky said.

Marcus said that a decade after the 2012 deep learning revolution, considerable progress has been made, but some issues remain.

He laid out four key aspects of cognition that are missing from deep learning systems:

Deep neural networks will continue to make mistakes in adversarial and edge cases, said Yejin Choi, computer science professor at the University of Washington.

The real problem were facing today is that we simply do not know the depth or breadth of these adversarial or edge cases, Choi said. My haunch is that this is going to be a real challenge that a lot of people might be underestimating. The true difference between human intelligence and current AI is still so vast.

Choi said that the gap between human and artificial intelligence is caused by lack of common sense, which she described as the dark matter of language and intelligence and the unspoken rules of how the world works that influence the way people use and interpret language.

According to Choi, common sense is trivial for humans and hard for machines because obvious things are never spoken, there are endless exceptions to every rule, and there is no universal truth in commonsense matters. Its ambiguous, messy stuff, she said.

AI researcher and neuroscientist, Dileep George, emphasized the importance of mental simulation for common sense reasoning via language. Knowledge for commonsense reasoning is acquired through sensory experience, George said, and this knowledge is stored in the perceptual and motor system. We use language to probe this model and trigger simulations in the mind.

You can think of our perceptual and conceptual system as the simulator, which is acquired through our sensorimotor experience. Language is something that controls the simulation, he said.

George also questioned some of the current ideas for creating world models for AI systems. In most of these blueprints for world models, perception is a preprocessor that creates a representation on which the world model is built.

That is unlikely to work because many details of perception need to be accessed on the fly for you to be able to run the simulation, he said. Perception has to be bidirectional and has to use feedback connections to access the simulations.

While many scientists agree on the shortcomings of current AI systems, they differ on the road forward.

David Ferrucci, founder of Elemental Cognition and a former member of IBM Watson, said that we cant fulfill our vision for AI if we cant get machines to explain why they are producing the output theyre producing.

Ferruccis company is working on an AI system that integrates different modules. Machine learning models generate hypotheses based on their observations and project them onto an explicit knowledge module that ranks them. The best hypotheses are then processed by an automated reasoning module. This architecture can explain its inferences and its causal model, two features that are missing in current AI systems. The system develops its knowledge and causal models from classic deep learning approaches and interactions with humans.

AI scientist Ben Goertzel stressed that the deep neural net systems that are currently dominating the current commercial AI landscape will not make much progress toward building real AGI systems.

Goertzel, who is best known for coining the term AGI, said that enhancing current models such as GPT-3 with fact-checkers will not fix the problems that deep learning faces and will not make them capable of generalization like the human mind.

Engineering true, open-ended intelligence with general intelligence is totally possible, and there are several routes to get there, Goertzel said.

He proposed three solutions, including doing a real brain simulation; making a complex self-organizing system that is quite different from the brain; or creating a hybrid cognitive architecture that self-organizes knowledge in a self-reprogramming, self-rewriting knowledge graph controlling an embodied agent. His current initiative, the OpenCog Hyperon project, is exploring the latter approach.

Francesca Rossi, IBM fellow and AI Ethics Global Leader at the Thomas J. Watson Research Center, proposed an AI architecture that takes inspiration from cognitive science and the Thinking Fast and Slow Framework of Daniel Kahneman.

The architecture, named SlOw and Fast AI (SOFAI), uses a multi-agent approach composed of fast and slow solvers. Fast solvers rely on machine learning to solve problems. Slow solvers are more symbolic and attentive and computationally complex. There is also a metacognitive module that acts as an arbiter and decides which agent will solve the problem.Like the human brain, if the fast solver cant address a novel situation, the metacognitive module passes it on to the slow solver. This loop then retrains the fast solver to gradually learn to address these situations.

This is an architecture that is supposed to work for both autonomous systems and for supporting human decisions, Rossi said.

Jrgen Schmidhuber, scientific director of The Swiss AI Lab IDSIA and one of the pioneers of modern deep learning techniques, said that many of the problems raised about current AI systems have been addressed in systems and architectures introduced in the past decades. Schmidhuber suggested that solving these problems is a matter of computational cost and that in the future, we will be able to create deep learning systems that can do meta-learning and find new and better learning algorithms.

Jeff Clune, associate professor of computer science at the University of British Columbia, presented the idea of AI-generating algorithms.

The idea is to learn as much as possible, to bootstrap from very simple beginnings all the way through to AGI, Clune said.

Such a system has an outer loop that searches through the space of possible AI agents and ultimately produces something that is very sample-efficient and very general. The evidence that this is possible is the very expensive and inefficient algorithm of Darwinian evolution that ultimately produced the human mind, Clune said.

Clune has been discussing AI-generating algorithms since 2019, which he believes rests on three key pillars: Meta-learning architectures, meta-learning algorithms, and effective means to generate environments and data. Basically, this is a system that can constantly create, evaluate and upgrade new learning environments and algorithms.

At the AGI debate, Clune added a fourth pillar, which he described as leveraging human data.

If you watch years and years of video on agents doing that task and pretrain on that, then you can go on to learn very very difficult tasks, Clune said. Thats a really big accelerant to these efforts to try to learn as much as possible.

Learning from human-generated data is what has allowed GPT, CLIP and DALL-E to find efficient ways to generate impressive results. AI sees further by standing on the shoulders of giant datasets, Clune said.

Clune finished by predicting a 30% chance of having AGI by 2030. He also said that current deep learning paradigms with some key enhancements will be enough to achieve AGI.

Clune warned, I dont think were ready as a scientific community and as a society for AGI arriving that soon, and we need to start planning for this as soon as possible. We need to start planning now.

VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact. Discover our Briefings.

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Artificial intelligence (AI) and machine learning have already made significant inroads into the media landscape, and they are poised to become even more dominant in the coming years. In fact, many experts predict that by 2023, AI and machine learning will be at the forefront of the media industry, driving innovation and shaping the way that we consume and interact with content.

There are a number of reasons why AI and machine learning will likely dominate the media landscape in 2023 and beyond. Here are a few key factors to consider:

In conclusion, AI and machine learning are poised to dominate the media landscape in 2023 and beyond. These technologies are already driving innovation, improving efficiency, and enhancing the user experience, and they are likely to become even more important in the coming years. As a result, media companies that are able to effectively leverage these technologies will be well positioned to succeed in the rapidly changing media landscape of the future.

Looking for help in this area? Reach out to AHOD.

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Analytics and ML performance and speed to results can be vastly improved when using a hybrid cloud that incorporates a modern database.

Businesses today are making analytics and machine learning a central part of their operations to help with everything, including improving efficiencies, hyper-personalizing customer services, and more. The dynamic nature of the workloads leads many businesses to move to hybrid cloud.

Hybrid cloud offers great scalability, cost savings, and the ability to move workloads to platforms that are optimized for the compute, storage, and data management needs of analytics and ML-intensive operations. In particular, performance and speed to results can be vastly improved when using a modern database that offerscapabilities, tools, and supportthat are more advanced than many on-premises technologies.

That is an area where working with a company like Vertica, a Micro Focus line of business, comes into play. The Vertica SQL database and in-database machine learning solutions support the entire predictive analytics process with massively parallel processing and a familiar SQL interface.

Vertica allows wide-scale use of analytics and ML throughout a business. That, in turn, helps deliver significant economic value to a business. Unfortunately, many companies need help with their particular move to hybrid.

Addressing geographically incurred latency

Another issue is how to make use of data and databases that already exists but may not be geographically close to cloud resources. The issue here is that when moving workloads to the cloud, latency due to geographic separation may be a problem. In particular, latency becomes especially important when analytics and ML routines are run on highly-optimized platforms such as Verticas.

To address this issue, the Vertica platform leverages technology from Vcinity. Vcinity uses patented technology to enable the Vertica platform to process geographically dispersed data across hybrid cloud environments as if Vertica and its data are co-located. It delivers LAN-like performance regardless of distance/latency and enables applications to access data, where and when it is created.

Verticas unified analytics platform and combined with other offerings and capabilities delivered via partnerships have use in a broad range of applications. In all cases, the business reaps significant benefits. Some examples include:

B2C marketing: Netcore is a global Martech product company that helps B2C brands create digital customer experiences with a range of products that help in acquisition, engagement, and retention. Netcores clients use its solutions to plan, execute, and monitor marketing campaigns across different channels such as Email, SMS, App, WhatsApp, and so on. Given limited budgets, the key ROI challenge for clients is to target the right customers, at the right time, on the right channels, and with the right message to maximize response rates and conversions.

To assist its clients, Netcore created Ramanan AI platform that analyzes huge datasets of historical and recent customer behavior to deliver smarter customer segmentation, improved targeting, and sophisticated predictive modeling.

As clients rapidly adopted Raman, Netcore was able to maintain the analytics performance customers expected and required using Verticas analytics platforms capabilities. In particular, the companys database was able to handle write- and read-intensive workloads in parallel without any lag or drop-in efficiency. In contrast, its existing implementations of MySQL and MongoDB were unable to handle this workload efficiently, leading to slower model refresh and analysis. One additional benefit of teaming with Vertica is that the solution easily scales without performance degradation.

Analytically-driven businesses: Verticateamed with H3C to deliver the benefits of cloud-native analytics to enterprise data centers. Specifically, Vertica and H3C integrated their offerings to help analytically-driven companies to elastically scale capacity and performance as data volumes grow and as machine learning initiatives become a business imperative all from within hybrid environments.

Vertica with H3C ONEStor enables businesses to adopt hybrid cloud for analytics wherever their data resides. Combining these two technologies offers fast analytics while simplifying data protection with easy backup and replication features.

The combined offering delivers high-performance analytics and machine learning with enterprise-grade object storage to enable organizations to address scalability needsfor now and in the future, leverage the separation of compute and storage architecture to address varying dynamic workload requirements, and simplify database operations.

eWallet app: Vertica is working with Vietnams largest e-wallet company, MoMo, to provide data analytics and machine learning for MoMos all-in-one super app, which is used for e-wallet and other FinTech services. The Vertica Unified Analytics Platform provides the company with actionable analytical insights.

MoMo needed a solution that offered the highest performance at extreme scale, the broadest analytical and machine learning capabilities, and complete support for multi-cloud and hybrid deployment to accommodate any future growth needs. Vertica met all of these requirements.

To put the requirements into perspective, as of May 2022, MoMo had 31 million users in Vietnam with 2 PB+ of data. It expects that data volume to double next year and the number of users to double over the next two years. Vertica provides MoMo with the flexibility to run its analytical workloads in the cloud, on-prem, as well as in hybrid environments, providing them with deployment flexibility regardless of where their future needs take them. Another factor when choosing Vertica was that the unified analytics platform combines the strengths of the data warehouse and the data lake ecosystem all in one ensuring high performance, scalable analytics, and machine learning, delivered at an overall lower total cost of ownership.

Hybrid cloud is well-suited to the dynamic demands of modern analytics and machine learning workloads. Increasingly, businesses are finding that one essential element of a hybrid environment for these workloads is a modern database.

The Vertica Unified Analytics Platform is just such a database. It is based on a massively scalable architecture with a broad set of analytical functions spanning event and time series, pattern matching, geospatial, and end-to-end in-database machine learning.

As such, Vertica enables many businesses to easily apply these powerful functions to the largest and most demanding analytical workloads, arming businesses and its customers with predictive business insights faster than other analytical databases or data warehouses in the market.

Critical to being part of a hybrid environment, Vertica provides its Unified Analytics Platform as SaaS on AWS, across all major public clouds, and on-premises data centers as a BYOL (bring your own license) model.

Learn more: https://www.vertica.com/what-is/hybrid-cloud/

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The Economic Impact of Transitioning to Hybrid Cloud for Analytics ... - RTInsights

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The metaverse is becoming one of the hottest topics not only in technology but in the social and economic spheres. Tech giants and startups alike are already working on creating services for this new digital reality.

The metaverse is slowly evolving into a mainstream virtual world where you can work, learn, shop, be entertained and interact with others in ways never before possible. Gartner recently listed the metaverse as one of the top strategic technology trends for 2023, and predicts that by 2026, 25% of the population will spend at least one hour a day there for work, shopping, education, social activities and/or entertainment. That means organizations that use the metaverse effectively will be able to engage with both human and machine customers and create new revenue streams and markets.

However, most of these metaverse experiences will be able to continue to progress only with the use of deep learning (DL), as artificial intelligence (AI) and data science will be at the forefront of advancing this technology. For example, deep learning algorithms are making computers better at gesture recognition and eye tracking, thanks to the latest developments in computer vision that enable natural interactions and better understanding of emotion and body language. As such technologies are an essential aspect of the metaverses immersive interface, deep learning technologies now aim to further enhance realistic AI storytelling, creative partnering and machine understanding.

Currently, the digital realities being developed by different companies have their own attributes and integrated functionalities, and are at different development levels. Many of these multiverse platforms are expected to converge, and this junction is where AI and data science domains, such as deep learning, will be critical in taking users to a new stage in their metaverse journey. Success in these endeavors will be contingent upon understanding vital elements of the algorithmic models and their metrics.

GamesBeat Summit: Into the Metaverse 3

Join the GamesBeat community online, February 1-2, to examine the findings and emerging trends within the metaverse.

Deep learning-based software is already being integrated into virtual worlds; some examples include autonomously driving chatbots and other forms of natural language processing to ensure seamless interactions. For another example, in AR technology, deep learning-enabled AI is used in camera pose estimation, immersive rendering, real-world object detection and 3D object reconstruction, helping to guarantee the variety and usability of AR applications.

In October, Meta announced the launch of its universal speech translator (UST) project, which aims to create AI systems that enable real-time speech-to-speech translation across all languages regardless of the users language. In addition, the companys recent advances in unsupervised speech recognition (wav2vec-U) and unsupervised machine translation (mBART) will aid the future work of translating more spoken languages within the metaverse.

All such implementations require massive training data and modeling, now made possible through deep learning methodologies. In addition, AI-based Web3 technologies are now being called upon to automate smart contracts and decentralized ledgers, and create universal blockchain technologies to enable virtual transactions.

Deep learning provides much higher accuracy [and] almost no false positives, and if properly implemented, eliminates data noise (corruption), Jerrod Piker, competitive intelligence analyst at Deep Instinct, told VentureBeat.

Piker said that such implementations could aid in improving the metaverse, as a deep learning model is trained on all available data, providing incredible results on image recognition and natural language processing.

Meta has applied this in translating code from one programming language to another. Since the metaverse is a wide and open world, automatically translating code can have a huge impact on seamless integration between different platforms within the metaverse, he said.

Likewise, Scott Stephenson, CEO and cofounder at Deepgram, believes that deep neural networks are more capable and sophisticated than neural networks with fewer layers.

Companies have an interesting opportunity for their customers and community to interact with their brand(s) in new and exciting ways, and deep learning-based artificial intelligence plays a major role in facilitating those experiences, said Stephenson.

He explained that companies can now have AI brand representatives trained on a companys unique linguistic style and product documentation wander about the metaverse, evangelizing whatever product or service the company seeks to promote.

Rather than giving them dozens or even hundreds of lines of pre-scripted dialogue (like what youd experience in most video games these days), theres no reason why a metaverse platform shouldnt be running a generative text chatbot in the background to drive conversation and engagement, he said.

Despite its promise and potential, the metaverse continues to face user-based risks, such as data security. Deep learning-based AI models could be instrumental in overcoming those challenges when integrated with legacy tools.

Securing sensitive data that is being created, sent and shared across the metaverse requires more advanced techniques than past data security efforts. Deep learning can provide excellent results on this front with its uncanny ability to accurately identify content, said Piker. For instance, ongoing inspection for certain sensitive data to ensure it is not being leaked outside of its intended channel is extremely important, and deep learning is unmatched in correctly and efficiently identifying digital content of all kinds, with a far superior false positive rate vs. other machine learning models.

Scott Likens, innovation and trust technology leader at PwC, said that many brands have started to see the metaverses actual business value as deep learning and AI converge with VR to provide a much deeper experience for the metaverse in the future.

The generation of assets in the metaverse now benefits from AI, as there is currently a lack of content and digital assets to fill the metaverse. In addition, with the advances in data collection through IoT, we can feed the data-hungry deep learning models to create lifelike yet synthetic worlds that are being used to help drive business strategy and more at a pace we cant match in the current workforce, said Likens.

Deep learning technologies are going to be highly important in terms of automation, says Patrik Wilkens, vice president of operations at TheSoul Publishing, whose universe of well-known channels includes 5-Minute Crafts, Bright Side and 123 GO!.

Progress that used to take hours and hours of human effort is now attainable with incredible efficiency. As tech companies and content creators utilize the best tech, incorporating deep learning into their processes, the manpower that was previously used to make things work can now be used on other things. This is especially important for creative domains, Wilkens told VentureBeat.

Wilkens further explained that his company, TheSoul, is currently utilizing deep learning-based algorithms for several metaverse use cases.

We are using deep learning-based artificial intelligence in our content right now to proofread, translate, [perform] quality assurance (QA) and build graphics. Were also in the development stage on a number of initiatives, including the 5-Minute Crafts marketplace within the metaverse, he said. That could work by your avatar walking into TheSouls shopping mall-style building, watching a craft video, and going to the AI assistant to help you purchase the materials needed to complete the project.

Adrian McDermott, CTO at Zendesk, believes that in 2023, we can expect to see deep learning and AI technologies power and scale customer self-service in the metaverse.

Businesses will expand the use of AI and automation to route and escalate urgent user issues in real time, ensuring the experience remains seamless, McDermott told VentureBeat. Large language models (LLMs) will play a role in helping brands understand customer needs in these new spaces, as well as generating potential responses to service requests. Self-service powered by deep learning-based AI models can unburden human agents by helping customers sort through straightforward questions more easily, freeing agents up to dig into the more difficult cases.

McDermott said that we would begin to see industries beyond retail and gaming begin to build or pilot metaverse experiences to stay competitive. Brands will be using the metaverse to not only engage with customers, but to build loyalty through digital collectibles, and automation will play a role in that journey.

Dont be surprised to see not only an expansion of digital storefront and concert experiences, but also increased use by the enterprise for hosting meetings, training and upskilling employees on critical job skills, he said.

Likewise, Wilkens predicts that in 2023, we can expect brands to begin building communities around virtual influencers.

Brands will focus on developing more meaningful content to engage their virtual influencers communities in an effort to be more human and connect with audiences authentically, he said. Additionally, we expect to see a rise of avatars. They will be everywhere especially in the metaverse and will evolve dramatically on platforms like Snapchat due to new features like avatar fashion and digital items coming in 2023.

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