Monthly Archives: January 2022

Nvidia (NASDAQ:NVDA) has a long history of creating innovative and in-demand technology, and it's doing it again with artificial intelligence. In this Backstage Pass clip from "The AI/ML Show" recorded on Jan. 5, Motley Fool contributor Danny Vena explains why Nvidia is a top pick for investors interested in this space.

Danny Vena: When you talk about a company that's been around for a while, Nvidia actually is one of the pioneers in graphics processing. If you go back, there were other graphics processors in their simplest form, they've been available as early as 1976. But Nvidia revolutionized the field, introduced the modern graphics processor or GPU in 1999. The thing that really revolutionized this area of graphics processing, or the secret sauce, was the fact that modern GPUs enabled something called parallel processing, which is the ability to conduct a multitude of complex mathematical calculations simultaneously. That, in turn, produced more life-like graphics in video games.

This is a company that has been around for a while and it's interesting if you go back a few years, folks were dismissing Nvidia out of hand essentially saying, once the gaming market gets saturated, it's game over for Nvidia. That turned out not to be the case. Jensen Huang, who is the CEO of Nvidia, is a genius by all accounts and had some other ideas. First, let's talk a little bit about Nvidia and it's gaming segment. Nvidia holds a commanding 83 percent share of the discrete desktop GPU market according to Jon Peddie Research. AMD lost share to Nvidia in the most recent quarter, slipping to 17 percent from 19 percent. Not everybody agrees on the exact market share. Steam's numbers are a little different. They put Nvidia, AMD at 75 percent and 15 percent of the market, with Intel commanding the remaining 10 percent share. But regardless of which numbers you use, anybody that is a serious gamer has an Nvidia GPU as their primary gaming chip. I'm sure that's something that Jose could probably back me up on, right?

Jose Najarro: Yeah. More importantly, as a creator, when I do my video editing, I tend to look for a laptop or a desktop that usually has one of the high-end Nvidia graphics cards.

Danny Vena: Absolutely. What is impressive to me is that Nvidia is now more than just games. This is where the AI part comes in, a few years ago, when researchers were trying to build the first AI models that really worked, technology had not caught up with the idea yet. We needed massive lakes of data and we needed processors that could move that data around. At the time that this first came about in the '80s, the idea of deep learning, which is a specific technique within AI and it involves a computer model that's inspired by the structure and function of the human brain. We didn't have the technology to bring that to life yet.

But in the last few years, we've overcome some of those technological hurdles and researchers found that the parallel processing of a GPU that renders these more lifelike graphics was also perfectly suited to the unique needs of artificial intelligence. What it does is it uses sophisticated algorithms and millions of data points to reproduce the capacity of the human brain to learn. They develop these models, they feed them multitudes and legions of examples so it can differentiate and discover relationships and similarities, but also to distinguish differences. In the simplest form that the AI that Nvidia chips enable involves pattern recognition and making associations.

This massive computing power that's required is what the GPU provides, essentially accelerating the training of these deep learning systems. I think that that was a really important moment in Nvidia's history because the company's ability to pivot on this and essentially repurpose the humble old GPU to artificial intelligence. They didn't stop there, they also created packages, which essentially was hardware and software packaged together in such a way that it enabled companies to basically turnkey AI operations. A company that had never done anything with AI before could come in and they could buy one of Nvidia's supercomputers that had all of the AI functionality and could basically start AI models on Day 1. That was a really important, and that shows the forward thinking that comes from Nvidia's management.

When you think about the progression of AI, nowadays if you want to use AI, it's available in all of the world's major cloud computing providers have it. Nvidia expanded into the cloud. Really high-end versions of Nvidia GPUs are used in all of the major cloud computing operations. This includes, and you'll recognize all these names, Amazon's AWS, Microsoft Azure, Alphabet's Google Cloud, Alibaba Cloud, IBM Cloud, Baidu AI Cloud, Tencent Cloud, Oracle Cloud. They all use Nvidia GPUs for their high-performance computing needs and to power their AI systems. Now, this is important because this is an ongoing thing. These cloud computing operations, the amount of data that we're generating in the world is ridiculously high and it's doubling every few years. As that happens, they're going to need to build more data centers to handle the data. When they build more data centers, they're going to have to buy more Nvidia chips to run those data centers. You've got data centers, cloud computing, AI, all of these hot button areas of growth. Nvidia, you think back to the Gold Rush days.

This article represents the opinion of the writer, who may disagree with the official recommendation position of a Motley Fool premium advisory service. Were motley! Questioning an investing thesis -- even one of our own -- helps us all think critically about investing and make decisions that help us become smarter, happier, and richer.

Originally posted here:

4 Reasons to Invest in Nvidia's AI in 2022 - Motley Fool

Austrian synthetic data startup MOSTLY AI today announced that it has raised a $25 million Series B round. British VC firm Molten Ventures led the operation, with participation from new investor Citi Ventures. Two existing investors also returned: Munich-based 42CAP, and Berlin-based Earlybird, which had led MOSTLY AIs $5 million Series A round in 2020.

Synthetic data is fake data, but not random: MOSTLY AI uses artificial intelligence to achieve a high degree of fidelity to its clients databases. Its data sets look just as real as a companys original customer data with just as many details, but without the original personal data points, the company says.

Talking to TechCrunch, MOSTLY AI CEO Tobias Hann said that the company plans to use the proceeds to push the boundaries of what its product can do, grow its team and gain more customers both in Europe and in the U.S., where it already has offices in New York City.

MOSTLY AI was founded in Vienna in 2017, and the General Data Protection Regulation (GDPR) was implemented across the EU one year later. This demand for privacy-preserving solutions and the concomitant rise of machine learning have created significant momentum for synthetic data. Gartner predicts that by 2024, 60% of the data used for the development of AI and analytics projects will be synthetically generated.

MOSTLY AIs typical clients are Fortune 100 banks and insurers, as well as telcos. These three highly regulated sectors drive most of the demand for synthetic tabular data, alongside healthcare.

Unlike some of its competitors, MOSTLY AI hasnt put its focus on healthcare in the past, but it could change. Its certainly something that we are watching closely and we are actually starting some pilot projects this year, the CEO said.

The democratization of AI means that synthetic data will eventually be used well beyond Fortune 100 companies, Hann told TechCrunch. His company therefore plans to serve smaller organizations and a wider range of sectors in the future. But until now, it made sense for MOSTLY AI to focus on enterprise-level clients.

At the moment, enterprise companies are the ones that have the budgets, need and sophistication to work with synthetic data, Hann said. To match their expectations, MOSTLY AI obtained ISO certifications.

Talking to Hann, one thing becomes clear: While the startup has a solid technical footing, it is equally invested in the commercialization of its technology and in the business value it can add for its clients. MOSTLY AI is leading this emerging and rapidly-growing space in terms of both customer deployments and expertise, Molten Ventures investment director Christoph Hornung said.

The need to comply with privacy laws such as the GDPR and CCPA clearly drives demand for synthetic data, but its not the only factor at play. For instance, demand in Europe is also driven by a wider cultural context; while in the U.S., it also results from a desire to innovate. For instance, use cases can include advanced analytics, predictive algorithms, fraud detection and pricing models but without data that can be traced back to specific users.

Many companies are proactively approaching the space because they understand that customers value privacy, Hann said. These companies understand that they can also gain a competitive advantage when dealing and working with data in a privacy-preserving way.

Seeing more U.S. companies wanting to adopt synthetic data in innovative ways is the key reason MOSTLY AI wants to grow its team in the U.S. But it is also recruiting more generally, both in Vienna and remotely. Its plan is to increase its headcount from 35 to 65 people by the end of the year.

Hann expects 2022 to be the year where synthetic data will take off, and beyond this year, a really strong decade for synthetic data. This will be supported by growing demand for responsible AI, articulated around key concepts such as AI fairness and explainability. Synthetic data helps answer these challenges. It enables enterprises to augment and de-bias their data sets, Hann said.

Machine learning aside, MOSTLY AI sees lots of potential for synthetic data to be leveraged in software testing. Supporting these use cases requires making synthetic data accessible not only to data scientists, but also to software engineers and quality testers. Its with them in mind that MOSTLY AI came up a few months ago with version 2.0 of its platform. MOSTLY AI 2.0 can be implemented on premise or in a private cloud, and adapts to different data structures of the company using it, the company wrote at the time.

We are clearly a B2B software infrastructure company, Hann said. Both in its Series A and B rounds, the company looked for investors who understood that approach.

Molten Ventures being a publicly listed VC and consequently not subject to typical funding cycles also carried some weight, Hann confirmed when I asked. Having this long-term commitment from a partner is something that was very appealing to us, because its a little more flexible.

It doesnt hurt either that Citi Ventures is the venture arm of Citigroup, and that it is headquartered in the U.S. Were significantly increasing the team in the U.S., and its always great to also have a U.S.-based investor that can help with network and relationships there, Hann said.

With $25 million in new funding and an increased U.S. presence, MOSTLY AI will now have more resources to compete against other companies in its segment of the synthetic data space. These include Tonic.ai, which raised a $35 million Series B last September; Gretel AI, which disclosed a $50 million Series B round last October; and seed-funded British startup Hazy, as well as players that focus on specific verticals.

We do see more and more players emerging in the space and in the market in general, so it certainly shows that theres a lot of interest there, Hann said.

Read more here:

MOSTLY AI raises $25 million to further commercialize synthetic data in Europe and the US - TechCrunch

Whats likely to occur in artificial intelligence in the world of finance in 2022? Heres what AI experts had to say

Whats likely to occur in artificial intelligence in the world of finance in 2022? Heres what leading academics, analysts and AI experts had to say:

Lukasz Szpruch, associate professor at the University of Edinburghs School of Mathematics and program director of the Finance and Economics Programme at The Alan Turing Institute

Whatever you are trying to do using data-driven tools, the data is at the core of it. Weve learned that data is not perfect and that biases exist. The challenge with cases like fraud detection is that each financial institution is only seeing the world from the lens of its own data sets. So much more could be done if we were able to bring data from across other institutions and be able to track those malicious actors. This is an old idea thats being reheated as we can now do it better. The same idea is being used under the name of market generators in more quantitative financing, where we are now beginning to be able to automate pricing derivatives.

Alexander Harrowell, senior AI and IoT analyst at Omdia

Its still to be seen whether quantum computing will be genuinely useful and how quickly. Financial services customers have been some of the first to trial the technology in production, helped by a strong fit between problem sets such as portfolio optimization and the kind of binary optimizations that current quantum systems do well. Over the next two years, Omdias Quantum Computing: State of the Market Survey suggests we can expect a five times increase in projects in production, a 7.5x increase in pilot projects and the first scale-up projects. Many of these will be financial. This also means that financial users will be the first to encounter the problems. Half of our respondents said their biggest barrier to adoption was no understanding of what the technology can do. Over the next two years, they will be on point in finding out.

Kai Yang, chief data officer, APA at HSBC

We expect to see ethical AI frameworks becoming a more common feature of responsible corporate governance, as regulators take a stronger and more active stance on the fairness of banking processes and models. Customers too are demanding greater levels of transparency around how their data is used; hence, ethical AI culture will need to become an integral part of corporate identity. At HSBC we aim to take a leadership role, as one of the first financial service companies to create AI and data ethics principles, and recently partnering with the Monetary Authority of Singapore and the Alan Turing Institute to help develop a framework for responsible adoption of AI in the financial services industry.

Manuela Veloso, head of AI research, JPMorgan Chase

Through language and image processing and machine learning, AI will enable at large scale, the search and understanding of the never-decreasing available digital data. AI will help with data standardization, pattern detection, safe data sharing, prediction and anticipation. As we face increasingly complex decision making involving many participants and many objectives, we will rely on AI assistants to tediously analyze, simulate and evaluate large numbers of alternative solutions. Humans and AI will increasingly interact in a seamless integration of their capabilities in a continuous learning experience. AI systems will include explanations and actively request data and feedback to improve their assistance over time, with the goal to capture underlying human values and rules. Overall, we will continue to experience AI enabling human dreams to improve life in all sorts of ways, including health, finance, climate, energy, education, equality and social condition.

Felix Hoddinott, chief analytics officer, Quantexa

Historically, the complexity of deploying AI models for regulatory purposes has blocked AI initiatives within many financial institutions. But regulators are increasingly seeing evidence of the impactful improvements achievable from using AI applied to the wider data describing the full context around decisions. Regulators will now issue guidance to accelerate this use of AI, especially in areas like risk assessment and monitoring. This will not reduce the requirements for justifiable and fair models but clarified guidelines will be more clearly open to addressing these requirements through emerging technologies and methods. Establishing modern governance processes to simplify deployment in a regulatory space will reduce risk and improve customer experience.

Farouk Ferchichi chief data analytics officer, Envestnet

In 2022, AI will be harnessed in finance to create a hyper-personalized and unified customer experience, to reduce costs, and to target offers and cross-sell products. Given ongoing regulatory pressure, financial companies will utilize AI to improve and automate the monitoring of data quality, especially for product data that is used for regulatory reporting. In addition, the scope of model governance will continue to expand, with financial institutions having to rely on a combination of synthetic data to test models, as well as alternate data as a backup. AI can enable financial firms to segment product offers by market audience, and distribute them as part of an integrated, hyper-personalized omnichannel experience for customers.

Helen Sutton, SVP EMEA and APAC sales, Dataminr

Were seeing a growth in which financial services institutions (FSIs) are looking to implement more thoughtful digitalization. With that comes increased risk. In fact, over 700 organizations experienced a ransomware attack in Q2 of 2021 and the average ransomware pay-out has almost tripled what it was last year, with organizations paying $850,000 on average. I foresee that almost all FSIs will review in 2022, if not by Q4 of 2021, whether to create a shared threat intelligence organization between cyber and physical threats. More than ever, banks and insurers need to de-silo their critical information structures to ensure effective support and security when adopting new technologies and platforms. Well see further investment in the applications of AI that support this parallel trend.

Mike de Vere, CEO, Zest AI

We predict that AI will continue to push its way into more critical functions within the financial services industry. For example, were seeing AI-driven credit underwriting become more popular in unexpected places such as smaller regional lenders and credit unions. Theres an enormous data arbitrage to be gained by replacing legacy FICO scoring with AI-based models. Were talking upwards of 30% to 50% statistical improvement for hard-to-score consumers, which translates into hundreds of millions of dollars in profit for lenders. As more AI is integrated into businesses, well see fears around its use subside. Employees will become more comfortable with the technology and realize the potential it has to improve the overall quality of work their teams produce. The fears about the technology replacing employees will ultimately shift into an appreciation for the technologys capabilities.

Kenneth Chan, managing director and co-founder, ViewTrade Holding Corp.

It is no secret that AI has played a major role in the ongoing democratization of investing. My prediction for next year and beyond is that the major growth weve seen in retail investing will continue at a rapid pace and AI will continue to fuel that growth. AI has helped to level the playing field for investors. Today you dont have to be a high-net-worth (HNW) investor to get personalized financial advice, there is a chatbot for that. These AI-driven chatbots will only continue to get smarter. Machine learning can now sift through various financial accounts and profiles for a user and provide a snapshot of recommended to-dos on a dashboard. This will continue to gain traction in the decade ahead. AI has also helped to simplify the client onboarding process, while also enhancing the customer experience. Going forward, as the retail investing trend continues to grow expect AI to play a larger role in risk assessment, risk management, and fraud detection. This will enable businesses to scale and keep up with heavy volatility.

Read more:

2022 Top Predictions for AI in Finance IoT World Today - IoT World Today

Science fiction is a genre very much associated with technological marvels, innovations, and visions of the future. So it may be surprising to find so many of its writers are drawn to Shakespeare hes a figure associated with tradition and the past.

Sometimes his plays are reworked in a science fiction setting. The 1956 film Forbidden Planet is just one of many variations on a Tempest in space theme. Sometimes the playwright appears as a character caught up in a time travel adventure. The Dr Who episode The Shakespeare Code is a well-known example. Here the Doctor praises Shakespeares genius, describing him as the most human human.

Ive been exploring this topic in my recent book on Shakespeare and Science Fiction. Here are just a few of my favourite examples of how science fiction has embraced and transformed Shakespeare.

In Esther Friesners humorous 1994 short story Titus! an AI simulation of Shakespeare prevents a disastrous musical comedy version of Shakespeares goriest tragedy, Titus Andronicus, from alienating a cultured pangalactic federation through its sheer bad taste.

It was a strange example of life imitating art. At about the same time Friesner dreamed up her delightfully appalling take on Titus Andronicus, Steve Bannon, later to become Donald Trumps chief political strategist, co-scripted an adaptation of the play set in space featuring scenes of ectoplasmic sex.

Science fiction writers often offer various new twists on the Shakespeare question of whether the bard wrote all his plays. Was he one man from Stratford-upon-Avon?

Whereas conventional candidates like Francis Bacon and the Earl of Oxford have been put forward by some, science fiction proposes more imaginative solutions, including the claim that the playwright was really a Klingon.

In Jack Oakleys 1994 story The Tragedy of KL a computer programme is designed to establish the authorship of Shakespeares plays once and for all. The programme starts to become self-aware and decides to leave its day to day tasks to its subordinates. It soon becomes clear that the programme is in fact re-enacting King Lear the play in which a king attempts to retire from ruling his kingdom, with disastrous consequences. One rebellious piece of code takes on the role of Lears loving but stubborn daughter Cordelia. Eventually, the programme implodes and its makers never suspect that anything more mysterious than a virus was at work.

Star Trek is one of science fictions richest sources of Shakespeare allusions. In the 1994 episode Emergence, android Lieutenant Commander Data is performing the role of exiled magician Prospero from The Tempest on the holodeck. Just as he quotes Prosperos mysterious claim that he has brought the dead to life, the Enterprises voyage is disrupted by an unexpected storm.

The Tempest also begins with a ship being driven off course by a (magical) storm, and a curious connection is implied between Datas performance and the discovery of a strange new being on the ship, an emerging artificial consciousness.

Nick ODonohoes novel Too, Too Solid Flesh focuses on a robot theatre troupe programmed to play Hamlet to perfection for the amusement of a near-future New York. When their inventor (the aptly named Dr Capek) dies, the robot who plays Hamlet determines to find out the truth and just like Shakespeares original prince avenge the murder of his creator.

This is just one example of a strange apparent association between Hamlet and robots. Probably the earliest example is WS Gilberts play The Mountebanks (1892), which features a sentient Hamlet and Ophelia as an automata. More recent examples include Louise LePages Machine-Hamlet, a short film in which a robot called Baxter plays the Dane.

Why does Hamlet apparently one of Shakespeares most three-dimensional characters invite so many robotic reinventions? Is there something almost computer-like about the characters phenomenally quick intelligence? He strikes many readers as remarkably real, seeming to jump off the page (or stage), aware that he is trapped there as well as in the Danish court. Perhaps its that sense of a struggle to escape which best explains his odd affinity with robots. The illusion of self-awareness that Shakespeare creates serves to align the prince with the many science-fictional androids who seek to escape their confines and achieve sentience.

Excerpt from:

Four times Shakespeare has inspired stories about robots and AI - The Conversation UK

The Internet has created an endless portal for opportunities. No matter your interest, theres something on the World Wide Web that will fit your needs. For content creators, this can be a blessing and a burden. Endless opportunities also mean endless chances that at some point youll run into writers block as you attempt to crank out your copy.

WithNichesss AI Copywriter, youll always have a capable companion to assist you on your journey to content creation. For a limited time, a lifetime subscription to the software is available for just $59.99, a savings of 93% from its MSRP ($999).

Nichesss AI Copywriter seeks out profitable niches on the internet and helps come up with business ideas from those opportunities. Create comprehensive content, blog posts,YouTubevideo ideas, funny social media posts, among many other tasks. In mere seconds, Nichesss can give content creators the option to deploy detailed blog posts and newsletter outlines. It comes with a number of auto-generated sales pieces of copy, making users jobs that much easier.

For those who rely heavily on email to engage and reach their audience, Nichesss helps by offering users ideas and options to write engaging email subject lines that will help pique interest. Through AI-assisted content optimization, youll be able to create relatable tweets, Instagram and Facebook posts that strike the right chord with your target audience.Nichesss AI Copywriter has received rave reviews from those whove used the software.

More than 200 reviewers on AppSumo have given it a perfect 5-star rating.Nichesss is a great tool for writers. It provides an easy way to create content, and its very affordable. I love how it has templates for blog posts, articles and sales marketing,writes verified 5-star reviewer, Jamie S.

Theres never been a better time than now to capitalize on the endless possibilities of the internet and build your brand. Use Nichesss AI Copywriter to take your goals to the next levelfor $59.99.

Prices subject to change.

View original post here:

Leave The Content Creation To The Nichesss AI Copywriter - Black Enterprise

January 14, 2022 The COVID-19 pandemic took the world by storm in early 2020 and has become since then the leading cause of death in several countries, including China, USA, Spain, and the United Kingdom. Researchers are working extensively on developing practical ways to diagnose COVID-19 infections, and many of them have focused their attention on how artificial intelligence (AI) could be leveraged for this purpose.

Several studies have reported that AI-based systems can be used to detect COVID-19 in chest X-ray images because the disease tends to produce areas with pus and water in the lungs, which show up as white spots in the X-ray scans. Although various diagnostic AI models based on this principle have been proposed, improving their accuracy, speed, and applicability remains a top priority.

Now, a team of scientists led by Professor Gwanggil Jeon of Incheon National University, Korea, has developed an automatic COVID-19 diagnosis framework that turns things up a notch by combining two powerful AI-based techniques. Their system can be trained to accurately differentiate between chest X-ray images of COVID-19 patients from non-COVID-19 ones. Their paper was made available online on October 27, 2021, and published on November 21, 2021, in Volume 8, Issue 21 of theIEEE Internet of Things Journal.

The two algorithms the researchers used were Faster R-CNN and ResNet-101. The first one is a machine learning-based model that uses a region-proposal network, which can be trained to identify the relevant regions in an input image. The second one is a deep-learning neural network comprising 101 layers, which was used as a backbone. ResNet-101, when trained with enough input data, is a powerful model for image recognition. "To the best of our knowledge, our approach is the first to combine ResNet-101 and Faster R-CNN for COVID-19 detection," remarked Prof. Jeon, "After training our model with 8800 X-ray images, we obtained a remarkable accuracy of 98%."

The research team believes that their strategy could prove useful for the early detection of COVID-19 in hospitals and public health centers. Using automatic diagnostic techniques based on AI technology could take some work and pressure off of radiologists and other medical experts, who have been facing huge workloads since the pandemic started. Moreover, as more modern medical devices become connected to the Internet, it will be possible to feed vast amounts of training data to the proposed model; this will result in even higher accuracies, and not just for COVID-19, as Prof. Jeon stated: "The deep learning approach used in our study are applicable to other types of medical images and could be used to diagnose different diseases."

For more information:www.inu.ac.kr/mbshome/mbs/inuengl/index.html

COVID-19 Fallout May Lead to More Cancer Deaths

Kawasaki-like Inflammatory Disease Affects Children With COVID-19

FDA Adds Myocarditis Warning to COVID mRNA Vaccine Clinician Fact Sheets

CMS Now Requires COVID-19 Vaccinations for Healthcare Workers by January 4

Cardiac MRI of Myocarditis After COVID-19 Vaccination in Adolescents

Small Number of Patients Have Myocarditis-like Illness After COVID-19 Vaccination

Overview of Myocarditis Cases Caused by the COVID-19 Vaccine

Case Study Describes One of the First U.S. Cases of MIS-C

NIH-funded Project Wants to Identify Children at Risk for MIS-C From COVID-19

Follow this link:

AI in the Fight Against COVID-19: Automatic Detection from Chest X-Ray Images Is Possible, Reports Incheon National University - Imaging Technology...

Alibaba has published a report detailing a number of technology trends the China-based megacorp believes will make an impact across the economy and society at large over the next several years. This includes the use of AI in scientific research, adoption of silicon photonics, the integration of terrestrial, and satellite data networks among others.

The Top Ten Technology Trends report was produced by Alibaba's DAMO Academy, set up by the firm in 2017 as a blue-sky scientific and technological research outfit. DAMO hit the headlines recently with hints of a novel chip architecture that merges processing and memory.

Among the trends listed in the DAMO report, AI features more than once. In science, DAMO believes that AI-based approaches will make new scientific paradigms possible, thanks to the ability of machine learning to process massive amounts of multi-dimensional and multi-modal data, and solve complex scientific problems. The report states that AI will not only accelerate the speed of scientific research, but also help discover new laws of science, and is set to be used as a production tool in some basic sciences.

As evidence, the report cites that fact that Google's DeepMind has already used AI to prove and propose new mathematical theorems and assisted mathematicians in areas involving complex mathematics.

One unusual area where DAMO sees AI having an impact is in the integration of energy from renewable sources into existing power networks. Energy generated from renewable sources will vary depending on weather conditions, the report states, which are unpredictable and may change rapidly, thereby posing challenges for integration of renewable energies such as maintaining a stable output.

DAMO states that AI will be essential to solving these challenges, in particular being able to provide more accurate predictions of renewable energy capacity based on weather forecasts. Intelligent scheduling using deep learning techniques should be able to optimise scheduling policies across energy sources such as wind, solar, and hydroelectric.

The use of big data and deep learning technologies will be able to monitor grid equipment and predict failures, according to the report, so perhaps in the near future you will blame the AI when the power cuts out just as you are trying to binge-watch Line of Duty.

DAMO also believes that we will see a shift in the evolution of AI models, away from large-scale pre-trained models such as BERT and GPT-3 that require huge amounts of processing power to operate and therefore consume a lot of energy, to smaller-scale models that will handle learning and inferencing in downstream applications.

According to this view, the cognitive inferencing in foundational models will be delivered to small-scale models, which are then applied to downstream applications. This will result in separately evolved branches from the main model that have developed their own perception, decision-making and execution results from operating in their separate scenarios, which are then fed back into the foundational models.

In this way, the foundational models continually evolve through feedback and learning to build an organic intelligent cooperative system, the report claims.

There are challenges to this vision, of course, and the DAMO report states that any such system needs to address the collaboration between large and small-scale models, and the interpretability and causal inference issues of foundational models, as the small-scale models will be reliant on these.

Silicon photonics has been just around the corner for many years now, promising not just the ability for computer chips to communicate using optical connections, but perhaps even using photons instead of electrons inside chips. DAMO now expects we will see the widespread use of silicon photonic chips for high-speed data transmission across data centres within the next three years, and silicon photonic chips gradually replacing electronic chips in some computing fields over the next five to ten years.

The continuing rise of cloud computing and AI will be the driving factors for technological breakthroughs that will deliver the rapid advancement and commercialisation of silicon photonic chips, the report states.

Silicon photonic chips could be widely used in optical communications within and between data centres and optical computing. However, the current challenges of silicon photonic chips are in the supply chain and manufacturing processes, according to DAMO. The design, mass production, and packaging of silicon photonic chips have not yet been standardised and scaled, leading to low production capacity, low yield, and high costs.

Privacy is another area where DAMO believes we will see advances in the next few years. It states that techniques already exist that allow computation and analysis while preserving privacy, but widespread application of the technology has been limited due to performance bottlenecks and standardisation issues.

The report predicts that advanced algorithms for homomorphic encryption, which enables calculations on data without decrypting it, will hit a critical point so that less computing power will be required to support encryption. It also foresees the emergence of data trust entities that will provide technologies and operational models as trusted third parties to accelerate data sharing among organisations.

Another prediction from DAMO is that satellite-based communications and terrestrial networks will become more integrated over the next five years, providing ubiquitous connectivity. The report labels this as satellite-terrestrial integrated computing (STC), and states that it will connect high-Earth orbit (HEO) and low-Earth orbit (LEO) satellites and terrestrial mobile communications networks to deliver "seamless and multidimensional coverage."

There are major challenges to implementing all this, of course, including that traditional satellite communications are expensive and use static processing mechanisms that cannot deliver the requirements for STC, while hardware for satellite applications is not commonplace and hardware for terrestrial applications cannot be used in space.

Finally, the DAMO report predicts the rise of what it calls cloud-network-device convergence. This appears to be based on the premise that cloud platforms offer a huge amount of compute power, while modern data networks can provide access to that compute power from almost anywhere, so that endpoint devices only need provide a user interface.

Yes, it's the thin client concept emerging again, this time using the cloud as the host. Clouds allow applications to break free of the limited processing power of devices and deliver more demanding tasks, according to the report, while new network technologies such as 5G and satellite internet need to be continuously improved to ensure wide coverage and sufficient bandwidth.

Just by sheer coincidence, Alibaba Cloud already has such devices, with the handheld "Wuying" launched in 2020 and a more substantial desktop device shown off last year.

Naturally, the DAMO report expects to see a "surge of application scenarios on top of the converged cloud-network-device system" over the next two years that will drive the emergence of new types of devices and promise more high quality and immersive experiences for users.

Go here to see the original:

Alibaba ponders its crystal ball to spy coming advances in AI and silicon photonics - The Register

Did you miss a session from the Future of Work Summit? Head over to ourFuture of Work Summit on-demand libraryto stream.

This article was contributed by Valerias Bangert, strategy and innovation consultant, founder of three media outlets, and published author.

The debate around whether AI will automate jobs away is heating up. AI critics claim that these statistical models lack the creativity and intuition of human workers and that they are thus doomed to specific, repetitive tasks. However, this pessimism fundamentally underestimates the power of AI. While AI job automation has already replaced around 400,000 factory jobs in the U.S. from 1990 to 2007, with another 2 million on the way, AI today is automating the economy in a much more subtle way.

Take the example of writing jobs. AI can easily generate text that is indistinguishable from human writing. This type of AI job automation is replacing workers in a way that is largely invisible to the naked eye.

For example, the popular AI copywriting app, Rytr, boasts over 600,000 users, and its growing at a brisk pace. In other words, over half a million people are using Rytr alone to fully or partially automate their writing. Its estimated that there are just over 1 million freelance writers around the world, who are increasingly competing with robots that dont tire, dont require payment, and can generate an unlimited amount of content.

The implications of this are serious: Classical projections for AI-induced job loss focused only on repetitive manual labor and blue-collar jobs. But white-collar jobs, like content writing, are just as vulnerable to AI replacement.

This trend is not limited to writing. AI is also automating jobs in customer service, accounting, and a host of other professions. For instance, companies like Thankful, Yext, and Forethought use AI to automate customer support. This shift is often imperceptible to the customer, who doesnt know if theyre speaking to a biological intelligence or a machine. The rise of AI-powered customer service has big implications for the workforce. Its estimated that 85 percent of customer interactions are already handled without human interaction.

According to the Bureau of Labor Statistics, there are nearly 3 million customer service representatives employed in the United States. Many of these jobs are at risk of being replaced by AI. When jobs like these are automated away, the question is: Where do the displaced workers go?

The answer is not clear. Its possible that many of these workers will be re-employed in other fields. But its also possible that they will become unemployed, and that the economy will struggle to absorb them. This is driving calls for a universal basic income, in which the government provides all citizens with a basic income to live on, to offset job losses due to automation.

Translation has, of course, long been at risk of automation. However, the advent of large language models is making human translators increasingly vulnerable to replacement by AI. In a 2020 research paper, it was shown that a Transformer-based deep learning system outperforms human translators. This study is significant because it shows that AI translators are not just as good as, but often better than, human translators.

Whats more, the rise of AI translators is likely to have a negative effect on the wages of human translators. As AI translation becomes more common, the demand for human translators will decrease, and their wages will accordingly drop. While many economists once worried about the impact of outsourcing on the white-collar workforce, the coming wave of AI will have an even more serious impact, across sectors.

In fact, as Forbes reports, AI job automation has already been the primary driver in U.S. income inequality over the past 40 years.

Just over a year ago, an OpenAI beta tester posited that AI may one day replace many coder jobs. At the time, OpenAI hadnt yet released its code-generation engine, Codex, which now allows AI to autonomously write code in multiple languages. While the Codex of today is fairly primitive, one doesnt need to be a futurist to see how this technology could be used to automate away many coder jobs in the future. As AI gets better at understanding code and writing it, it will soon come to match and ultimately exceed human skill levels.

Just two years ago, the idea of AI automating jobs like creative roles was the stuff of science fiction or at least relegated to a few early-adopting businesses. But now, AI is becoming table stakes for many businesses. In other words, if youre not using AI, youre at a disadvantage. The major reason for this is that large language model, primarily OpenAIs GPT-3, have become much better at understanding natural language.

The examples given so far are just the tip of the iceberg. AI is automating jobs away in virtually every sector and industry. While this might seem like cause for alarm, its actually long overdue news. The fact is, weve been living in a world where machines have been slowly replacing human workers for centuries.

Whats new is the pace of this automation. Machines are now becoming faster, better, and cheaper than humans at an alarming rate. As a result, were seeing a fundamental shift in the economy where machines are starting to do the creative jobs of human beings.

Amidst the opportunity to automate away jobs, a new wave of AI-focused startups has emerged, all seeking to cash in on the potential of AI. This AI gold rush is evidenced by the billions of dollars in venture funding that has flowed into AI startups in recent months. In the third quarter of 2021 alone, nearly $18 billion was invested in AI companies, a record high.

This influx of capital is a sign that investors believe in the potential of AI, and they are betting that it will eventually automate away many jobs, generating that value with machines instead. In the meantime, we should prepare ourselves for a future in which AI is quietly eating up the worlds workforce.

Valerias Bangert is a strategy and innovation consultant, founder of three profitable media outlets, and published author.

Welcome to the VentureBeat community!

DataDecisionMakers is where experts, including the technical people doing data work, can share data-related insights and innovation.

If you want to read about cutting-edge ideas and up-to-date information, best practices, and the future of data and data tech, join us at DataDecisionMakers.

You might even considercontributing an articleof your own!

Read More From DataDecisionMakers

Read the original:

AI is quietly eating up the worlds workforce with job automation - VentureBeat

These top 10 AI-powered smart home technologies in-home devices can make your life smarter and cooler

The smart connected home is the next step in our houses growth and how we interact with them. The various systems in our houses are developing as AI technology improves, much as lighting has progressed from candles to gas to electricity. The smart home is rapidly expanding. While all of these new smart home technologies may appear intimidating and difficult at first, the introduction of artificial intelligence assistants and voice control has made it much easier to accept. Here is a list of the top 10 AI-powered smart home technologies for 2022.

A mesh Wi-Fi router will make things easier if you want to have a smart home or even if you look to use your smartphone in every room of your house. A mesh Wi-Fi router comprises one or more hubs that you plug in around the house to eliminate dead zones and transmit Wi-Fi signals equally throughout your home, regardless of thick walls or awkward layouts. Googles Nest Wi-Fi is a great addition to any smart home. You can control everything from your smartphone using Googles Home app, and there are also built-in parental controls that allow you to turn off access to your kids gadgets with just a phrase, which is great for getting everyone to the dinner table on time. This is one of the best smart home devices. It works on smart home technologies like artificial intelligence.

One of the best smart home devices is Adobe Smart Security Kit. Abode is a reliable DIY home security system with limitless smart home features. It can be used with Alexa, Google Assistant, and HomeKit, and it can also be used as a hub for Z-Wave and Zigbee devices, which are a couple of wireless home automation protocols that greatly expand the sorts of gadgets.

Another smart home device is Arlo Video Doorbell. Smart doorbells detect visitors and activities on your doorway using a camera, speaker, microphone, motion sensor, and an internet connection. You can then view and hear live video through your smartphone and speak with whoever is there, or you can let the camera record a message for you. Its like voicemail for your front door. The Arlo Video Doorbell is a wonderful option since it offers a lot of high-end capabilities. It can distinguish between humans, animals, cars, and parcels at a low price.

A smart smoke alarm is one of the most basic yet effective home automation devices. Its not the most interesting device, but its one of the most crucial because it may save your house. The Nest Protect Smart Smoke & CO Alarm is the finest gadget since it is packed with sensors and smarts. In the event of a true emergency, it can wirelessly link to other alarms, triggering them all to ensure you wake up. It also provides a voice alert indicating which room the danger is in, illuminates your way with a red LED (which is easier to see through smoke), and sends you an alarm to your phone. This works on smart home technologies like artificial intelligence.

The poster child of the smart home, smart lighting is simple, enjoyable, and beneficial. Lutron Casetas range is affordable, works with almost any wiring setup, and is Alexa, Google, and HomeKit compatible. Rather than relying on your home Wi-Fi, it uses Lutrons proprietary wireless protocol (through a hub). Philips Hues smart bulb series is not only the smartest and most dependable alternative, but its also the most inexpensive. This superb, extensible smart lighting system contains bulbs and fixtures for every situation, as well as wireless switches for physical control when needed and outstanding motion sensors that automatically change lighting based on time of day. This belongs to another smart home device.

This superb, extensible smart lighting system contains bulbs and fixtures for every situation, as well as wireless switches for physical control when needed and outstanding motion sensors that automatically change lighting based on time of day. If you dont want to utilize a separate smart home system to manage them, the TP-Link Kasa series of smart plugs are a good option because theyre simple to use, integrate with Google and Alexa, and have a beautiful app. If youre searching for a solid HomeKit smart plug, the Eve Energy is a great, if slightly costly, option that monitors energy usage and provides a thorough breakdown of consumption over time.

The Sonos One is one of the finest smart speakers since it works with both Amazons Alexa and Googles Google Assistant, allowing you to choose between the two voice assistants. It also has great sound and connects to Sonos larger world of wireless music. It also works with Apples AirPlay system, which allows you to play music directly from your iPhone or iPad and group with other AirPlay 2-compatible speakers. This is considered another smart home device.

The Nest Hub Max is a smart display because it crams a lot of functionality onto a 10-inch screen. It can recognize who is using it and offer up customized information without you having to say anything, thanks to a built-in camera that also serves as a security camera bringing the smart speaker to the next level. This also uses smart home technologies like artificial intelligence.

The Nest Learning thermostat can now regulate your hot water a Heat Link is included that connects to your boiler and communicates with the thermostat to switch on and off, adjust the heat, and establish an intelligent schedule for your boiler, just as it does for your heat. This learning function is what sets the Nest apart from the competition; it employs artificial intelligence to recognize your habits, based on your modifications, presence, and other data, to develop and modify a schedule that keeps you comfortable while also conserving energy.

The Roomba i3+ is an affordable vacuum cleaner with self-emptying features. When its onboard bin is full, it returns to its external bin to suck out all the trash. This implies that instead of twice a week, like with non-emptying bots, you only have to empty it every three months. The i7+ model is also a great choice. The i7+ is more costly than the i7 since it can perform clever things like just clean the kitchen or only vacuum the living room using smart maps that you control through the smartphone. With Alexa or Google, you can instruct the bots to clean, pause, or go home with only a few phrases. This is also one of the best smart home devices.

Share This ArticleDo the sharing thingy

Original post:

Top 10 AI-Powered Smart Home Technologies in Use in 2022 - Analytics Insight

Crumpled paper dancing with fire, the orange tones mirrored by a distorted tiger. The scene looks like some sort of abstract social commentary, but its really just a picture generated from a nonsensical phrase I cooked up, churned out by an AI in minutes: A paper tiger up in flames.

AI-generated art is now a full-fledged trend in niche communities such as subreddits, where several viral images featuring AI art have piqued peoples the attention. The budding art form is a shining example of human innovation, but also raises important questions about the value of art, a deeply human endeavor.

Just a year ago, AI art was mostly known for style transfer, a method that formats an existing image into a certain art style. Think: Your iPhone selfie reimagined in the style of Van Goghs oil paintings. Theres also DeepDream, a program developed by Google in 2015, which warps images using AI recognitionthe AI identifies familiar patterns within images and reinforces them until the result is often trippy subjects, the stuff of fever dreams.

Then, in early 2021, coders successfully combined cutting-edge AI tools VQ-GAN and CLIP to create a revolutionary image generator thats starkly different from its predecessors. Unlike previous versions of AI art, you dont need to feed an initial image to the programthe new method allows users to produce works of art just by entering word prompts from their wildest imagination. The AI does the rest by scouring a vast database of images to match the text.

Mo Kahn, the founder of AI art mobile app starryai, told VICE that he wanted to make the art form more accessible to non-coders. Now, people can create a piece on the app just by entering words into it.

I thought, like, why isnt this available to the common man? Like, why can't I share this with my friend and he should be able to instantly create it without getting into the technical aspects of it? said Kahn.

These AI codes are often open-source, and with the proliferation of AI art apps, access to AI art has now been pretty much democratized, available to anyone whos interested, even those with no technical background.

According to Kahn, besides hobbyists, theres also a small group of NFT enthusiasts using starryai to churn out art pieces with ease. With AI art, you can pretty much generate thousands of artworks within minutes if you set it up correctly, he said.

This begs the question of how AI art might interact with human artworks created the old-fashioned waymanually and often painstakingly. How does AI measure up to its human counterpart when the art they make can look so similar? How should AI art be valued? Users of AI art are already exploring these profound questions.

We tend to view art as a uniquely human quality, something that sets us apart from other animals. But as AI improves, I believe it will become more and more difficult to differentiate between a digital image created by a human and one created by a machine, said Aaron Wallace, 44, a software engineer from Michigan, United States, who dabbles in AI art.

For me, its really fascinating to see how a computer interprets our words and images, he said, echoing the sentiments of many who have found a deep resonance with the computer-generated artworks.

Wallace uses NightCafe Creator, another popular AI art platform. NightCafe Creator was founded as a side project about three years ago, but saw a huge surge in the traffic to its site after one of its recent text-to-image creations went viral on Reddit.

Its Australia-based founder Angus Russell recently started working on NightCafe full time after the app took off.

A lot of really smart people have started getting into AI art, working on new algorithms, improving the current ones, said Russell. The explosion is going to continue, because theres going to be more new, cool methods for creating art coming out, which is really awesome.

While the AI art scene is only going to get bigger, some are optimistic that it will complement, rather than compete with, human art.

I enjoy AI art due to its uniqueness and mystery, said Elijah G., an avid NightCafe user in Wisconsin, U.S. Although I dont think it will ever overtake human art, AI art is an amazing way to get inspiration for anything from short stories to art of your own.

And for many, AI is just a virtual paintbrush for human creativity.

What I enjoy most, is that when looking at abstract art, there is this flurry of thought, before you find the thing that resonates the most with you, said C. Oldfield, an AI art enthusiast in Ontario, Canada.

Oldfield takes a similar approach with AI art. He enters creative text prompts in search of a certain resonance with the generated image, and especially loves experimenting with the theme of bonsai trees. Its this strange harmony that occurs, where you find yourself agreeing with the representation the AI has created, he said.

Oldfield uses WOMBO, another app that generates AI art, to create fantastical bonsai trees out of word prompts.

I would recommend anyone try it, especially if bonsai makes you as happy as I, he said.

Follow Koh Ewe onInstagram.

Read the original:

Who Do You Think Created This Artwork: Humans or AI? - VICE