UNION CITY, Calif., July 17, 2023 /PRNewswire/ -- Anviz, a leading provider of professional and converged intelligent security solutions, recently announced the launch of IntelliSight, its latest video surveillance offering that harnesses the power of distributed cloud and 4G technology to create an all-in-one security solution that delivers unmatched versatility, security, and data analytical capabilities. Now, User can enjoy One-year free cloud storage (7-day event-based video retention).

The Anviz IntelliSight cloud video surveillance management solution combines Anviz's proprietary cloud-based distributed video surveillance management platform with its iCam series artificial intelligence (AI) cameras to provide customers with a comprehensive and flexible surveillance performance. Equipped with best-in-class video analytics and classification, the solution is an ideal option for small and medium-sized companies across various industries, including logistics, education, healthcare, and retail.

"The birth of the IntelliSight solution marks a milestone in our years of effort to provide reliable and versatile security systems for global users," said Mike, Product Manager of IntelliSight. "We believe the solution, whose development is built on our industry-leading security solutions that have proven successful in the global market, will fill the market gap where customers are seeking an all-in-one cloud-based video surveillance system that can safeguard their properties without adding unnecessary costs to their budgets."

Built for Simple Deployment and Scalability

The IntelliSight solution greatly benefits both small to medium sized business installations by eliminating the need for redundant, complex on-site hardware that is traditionally used for setting up a CCTV system, streamlining deployment steps, and keeping costs to a minimum for users. While customers can simply connect the cameras directly to the internet for effortless and fail-safe surveillance, the solution also allows them to easily scale their surveillance system without the extra process of storage and network device installation and debugging.

Immediate Access from Mobile Devices

IntelliSight's cloud-based architecture means that users have the freedom to easily access the surveillance system from anywhere, at any time. Via the Internet and the efficient P2P transmission protocol developed by Anviz, users have the option to view real-time video monitoring and manage devices at home and the office without any restrictions, coupled with a purpose-built mobile that allows for effortless remote access and on-the-go control, keeping users connected 24/7 and giving them peace of mind with greater convenience and easier operability.

Expanded Storage with Cloud Data Backup

IntelliSight enables users to store important event footage securely in cloud servers that provide expandable and flexible storage options, removing the need for extra hardware installation for media data. Additionally, IntelliSight's cloud-based storage reduces the risk of data loss in case of local device failures, with features such as data redundancy and disaster recovery offering an added guarantee for data security.

Advanced Video Analytics Powered by AI

Leveraging the state-of-the-art AI capabilities of Anviz surveillance cameras, the IntelliSight system can deliver advanced data analysis functionality to greatly enhance the efficiency and accuracy of security systems. The smart features of the systems can identify and categorize suspicious activity, categorize objects, and provide critical, timely information that enables users to quickly identify and respond to potential risks, streamlining their security operations while providing all-around protection for their assets.

"One of the biggest differentiators that sets Anvizapart from its competitors is its product's technological and architectural advantages, which also allows us to pioneer the development of a new generation of security systems powered by AIoT and cloud technology. Having been encouraged by the early adopters of the IntelliSight solution who have said it has exceeded their expectations in terms of costs, quality, and simplicity, we hope this solution will pave the way for our entry into the North American market, which is another springboard towards the global $30 billion surveillance system market,"Mike added.

Contact:Title: Marketing SpecialistName:Nic WangPhone: +86 15541141093Email: [emailprotected]com

SOURCE Anviz Global

See the original post:
Anviz Reveals IntelliSight, a Cloud-Based Distributed Video ... - PR Newswire

By Emily Bary

The 'obvious flagship AI company' can keep racking up stock gains even with its roaring run to start the year, analysts say

Nvidia Corp. continues to rack up bullish endorsements as analysts contemplate the vast opportunities ahead for the leader in artificial-intelligence chips.

Nvidia (NVDA) "is the obvious flagship AI company, whose decisions over the last two decades have positioned it for long-term benefits," wrote Melius Research analyst Ben Reitzes, who initiated coverage of the stock late Monday with a buy rating and $625 price target.

The company is reminding him of Apple Inc. (AAPL) "with a full-stack approach that in our experience tends to deliver an outsized profit share in the industry for longer than expected once the ball starts rolling downhill due to developer support and becoming an industry standard."

See also: Nvidia's stock could have a pathway to $600, Citi says

While Nvidia shares have soared more than 200% so far this year as the now-trillion-dollar company has solidified its AI positioning in Wall Street's mind, Reitzes suggests would-be investors haven't missed the boat.

"On the rare occasion a company like this comes along, we caution investors not to get caught up in [an] arbitrary market-cap milestone and [to instead] focus on sustainable long-term earnings power," he wrote. "Apple has also shown us ... sometimes the consensus can keep being right -- and keep working when the business model is right."

Read: Nvidia could follow 'blowout' outlook with more massive upside, analyst says

Meanwhile, BofA Securities analyst Vivek Arya became more upbeat about Nvidia's stock Tuesday, boosting his price objective on the stock to $550 from $500 while keeping a buy rating.

The upcoming earnings season could lead chip stocks to diverge between the "haves" and "have-nots" of AI, and Arya likes Nvidia's stock setup even more in that scenario.

"The next few quarters could see greater dispersion among chip stocks based on real vs. overstated/perceived AI benefits," Arya wrote Tuesday, calling Nvidia a player that "can hold its dominance." He expects the company to keep a 75% to 80% share of the market for accelerators.

More from MarketWatch:Amazon, Microsoft and Google cloud services bet heavily on AI, but do their customers even want it?

Arya noted that Nvidia's accelerators for servers are critical to driving intensive AI workloads and that the market opportunity is still ripe, since only around 10% of cloud servers are currently "accelerated," adding that "we are in just the early stages of AI investment."

He also highlighted that Nvidia houses other AI products including networking and software offerings that can help customers position themselves for the world of AI.

Read: AMD may suffer from elevated expectations -- but Wells Fargo is warming to Intel's setup

-Emily Bary

This content was created by MarketWatch, which is operated by Dow Jones & Co. MarketWatch is published independently from Dow Jones Newswires and The Wall Street Journal.

(END) Dow Jones Newswires

07-18-23 1107ET

See the original post here:
Nvidia is showing shades of Apple and its stock could hit $625, says ... - Morningstar

Chinese hackers intent on collecting intelligence on the United States gained access to government email accounts, Microsoft disclosed on Tuesday night.

The attack was targeted, according to a person briefed on the intrusion into the government networks, with the hackers going after specific accounts rather than carrying out a broad-brush intrusion that would suck up enormous amounts of data. Adam Hodge, a spokesman for the White Houses National Security Council, said no classified networks had been affected. An assessment of how much information was taken is continuing.

Microsoft said that in all, about 25 organizations, including government agencies, had been compromised by the hacking group, which used forged authentication tokens to get access to individual email accounts. Hackers had access to at least some of the accounts for a month before the breach was detected, Microsoft said. It did not identify the organizations and agencies affected.

The sophistication of the attack and its targeted nature suggest that the Chinese hacking group was either part of Beijings intelligence service or working for it. We assess this adversary is focused on espionage, such as gaining access to email systems for intelligence collection, Charlie Bell, a Microsoft executive vice president, wrote in a blog post on Tuesday night.

Although the breach appeared to be far smaller in scale than some recent intrusions like the SolarWinds hack by Russia in 2019 and 2020, it could provide information useful to the Chinese government and its intelligence services, and it threatened to further strain relations between the United States and China.

The vulnerability the hackers exploited appeared to be in Microsofts cloud security and was first detected by the U.S. government, which immediately notified the company, Mr. Hodge said.

Inside the government, the attack showed a significant cybersecurity gap in Microsofts defenses and raised serious questions about the security of cloud computing, the person briefed on the intrusion said. The government has been moving data to the cloud, which promises better access to information and improved security, because pushing out patches to vulnerabilities is faster. The U.S. also operates classified cloud servers, but they have more security protocols in place.

The person briefed on the intrusion said that government security requirements should have prevented the breach, and that Microsoft has been asked to provide additional information about the vulnerability.

We continue to hold the procurement providers of the U.S. government to a high security threshold, Mr. Hodge said.

The hack comes at a delicate point in U.S.-China relations, as the Biden administration seeks to cool tensions that have been aggravated in recent months by several incidents including the transit of a Chinese spy balloon across the United States. It could increase criticism that the Biden administration is not doing enough to deter Chinese espionage.

Cliff Sims, a former spokesman for the director of national intelligence in the Trump administration, said China had been emboldened because President Biden had not confronted Beijing over its attempts to influence recent elections.

We need to have some serious conversations about how much hacking well tolerate before taking action, Mr. Sims said.

Mr. Bell, in the blog post, said that people affected by the hack had been notified and that the company had completed efforts to mitigate the attack. But government officials are continuing to ask the company to provide more details of the vulnerability and how it occurred, according to the person briefed on the intrusion.

Microsoft said it was told of the intrusion and compromise on June 16. The companys blog post said the Chinese hacking group first gained access to email accounts a month earlier, on May 15.

Microsoft did not say how many accounts it believes might have been compromised by the Chinese hackers.

China has one of the most aggressive and most capable intelligence hacking operations in the world.

Beijing has, over the years, carried out a series of hacks that have succeeded in stealing huge amounts of government data. In 2015, a data breach apparently carried out by hackers affiliated with Chinas foreign spy service stole huge numbers of records from the Office of Personnel Management.

In the SolarWinds hack, which took place during the Trump administration, Russian intelligence agencies used a software vulnerability to gain access to thousands of computer systems, including many government agencies. The hack was named after the network management software the Russian agencies had exploited to get into computers around the world.

Read more:
Chinese Hackers Breached Government Email Accounts, Microsoft ... - The New York Times

A week after releasing beta 5, Apple is now seeding iOS 16.6 RC to registered developers. Even though the Cupertino firm nears the release of this upcoming iOS 16 update for iPhone users, its unclear the new features expected for this operating system, besides important bug fixes and security updates.

Sign up for the most interesting tech & entertainment news out there.

By signing up, I agree to the Terms of Use and have reviewed the Privacy Notice.

After a small iOS 16.5 update, Apple is still sharpening its software as the company preparesiOS 17, iPadOS 17,tvOS 17, watchOS 10, and macOS Sonoma for a fall release.

An early iOS 16.6 beta build added references forContact Key Verification, according toiOS developer Steve Moser.

Message Contact Key Verification is a feature announced by the company at the end of 2022. While Apple has already rolled out Security Keys for Apple ID and Advanced Data Protection for iCloud during the iOS 16 cycle, the firm still had to release that other function.

For those unaware, iMessage Contact Key Verification lets users verify if they are communicating only with whom they intend. This is especially helpful for journalists, human rights activists, and government members.

Apple says that conversations between users who have enabled iMessage Contact Key Verification receive automatic alerts if an exceptionally advanced adversary, such as a state-sponsored attacker, were ever to succeed breaching cloud servers and inserting their own device to eavesdrop on these encrypted communications. And for even higher security, iMessage Contact Key Verification users can compare a Contact Verification Code in person, on FaceTime, or through another secure call.

As of now, its unclear if iOS 16.6 will bring this feature, but Apple has already started testing it. In addition, Steve Moser says Apple continues to work onSports related features in the TV app, although he couldnt disclose whats new.

Besides iOS 16.6, Apple is also seeding the RC versions of iPadOS 16.6, watchOS 9.6, macOS 13.5, and tvOS 16.6.BGRwill let you know whats new when we learn more about these operating system updates.

See the article here:
iOS 16.6 RC is now available to developers with bug fixes and ... - BGR

In God we trust, all others bring data. William Edwards Deming

The main problems that farmers face on an everyday basis, such as climate changes, manual time-consuming tasks, and changes in consumers everyday dietary are presented in the article. Along with possible solutions that Machine Learning and Data Engineering can offer by applying such tools as IoT, data collection, computer vision, blockchain, and Augmented Reality.

Impact of cultural transformations on Productivity in Agriculture

Over the past centuries, human beings have gone through certain transformations that led to agricultural revolutions afterward. Each revolution played a key role in the increase of production and optimization of the production process. A long way from the first revolution to the fourth traces a path from hunting and gathering to digitalization and artificial intelligence. Lets dive into details, there are four agricultural revolutions:

First agricultural revolution (1700- onwards). This revolution is characterized by stationary farming with core principles mainly based on manual labor, horsepower, and simple tools, which means that productivity remained relatively low during that time period.

Second agricultural revolution (1914-1980s). At this time happens a shift from natural nitrogen supplementation to synthetic fertilizers. The introduction of crop rotation and drainage dramatically increased crop and livestock yields, improved soil fertility, and reduced fallow. An increase in production and a decrease in labor demand led to migration and urban expansion.

Third agricultural revolution (1920s-present). This revolution is all about combustion engines and rural electrification, the introduction of biotechnology, and genetic engineering, alongside computerized programs. The result of this revolution we could observe nowadays in the markets.

Fourth agricultural revolution (the 1970s onwards). This revolution made a transition from industrial production to a digital model that optimizes production processes, reduces time and cost, and enhances customer value. At this point, the agricultural industry started to operate with keys like the Internet of Things, Big Data, Artificial Intelligence, cloud computing, remote sensing, ingestion, and processing of big data into Data Lakes as a foundation for the decision-making approach.

The first and Second agricultural revolutions show a transition from manual labor to production, while the third and fourth revolutions show the importance of computerization and the collection of data. Nonetheless, there are still enormous problems in the agricultural sector of economics that seek solutions in Machine Learning.

Problems of traditional agriculture and the Role of Machine Learning

Demand for agricultural production has highly increased over the last year and ought to be one of the major causes of inflation all over the world in the nearest future. In the meantime, intensive agricultural yield increase is limited due to a number of external reasons:

A limited global land surface that is suitable for cultivation based on climate conditions, good soil, and urban development. According to up-to-date statistics, approximately 40% of the land is covered by jungles, deserts, urban places, or other natural land states such as forests. So, very little land is left for agricultural expansion.

Constantly changing consumer dietary habits and patterns push farmers to shift from one type of production to another. For example, demand for meat products is rising rapidly in societies due to inequality among the population.

Climate changes and natural disasters that have increased in the last century are likely to result in more extreme weather patterns, with average temperatures increasing, resulting in fluctuating yields and production shortfalls.

The application of Machine Learning in the agricultural sector can smooth the above-mentioned problems. In order to better understand the interference between these two fields, lets look at the example. Lets imagine that there is a farmer who relies mainly on calculations of input efforts and output yields. This farmer forecasts his/her profit based on scientific calculations. At the same time, he/she operates with data from sensors on the machinery, such as crop and GPS data, whilst other data is retrieved from a drone, and correlated against GIS information. At the same time, this farmer can also observe pricing data, livestock position, and demand for his product based on third-party cloud servers (diagram 1). All this together creates a picture of the potential crop value and demand. Also, weather forecast comes from open sources and clearly predicts conditions for the upcoming week. At the same time, sensors detect moisture in the ground and check the health status of plants and animals. All this data is gathered and stored for future analysis.

Tracking of every product entity is simple and can be clearly observed on the dashboards for the farmer and the final customer. Combining all this information together could save a lot of effort, help organizations work more efficiently, and solve the main problems that farmers face nowadays.

Integration of Machine Learning and Data Engineering into Agriculture

The covid-19 pandemic had a huge impact on Agriculture and at the same time on the development of Machine Learning and Data Engineering. The main goal of the integration between Agriculture and Machine Learning is to increase final crop yield, save efforts and resources, and help control every step of plants and animals growth. There is a number of techniques in Machine Learning that help collect agricultural data and ease the process for farmers. The following applications could be of great use for the development of communication between data collection and actual production.

1) A unified protocol

It is useful to have a single, unified protocol for cross-manufacturer compatibility of electric and electronic components. All mechanical and automotive devices have to be combined like a LEGO piece into one big machine. All parts of the final construction should communicate with others via protocols. This unified protocol is based on the International Standard ISO 11783 and started to be applied all over the world in 2008.

2) Internet of Things (IoT)

Different devices in a system have to be connected to the Internet and can interact with one another in real-time (diagram 2.) The number of sensors and their application is growing every year and will probably be around 250 billion in the next 5 years. According to this incredible number of sensors development of software products, it becomes a non-trivial task to collect and store information in a single place.

3) Drones and remote sensing

Development in information technology and agricultural science has made it possible to merge drones and Sensing, leading to the rise of precision farming. Such a scheme brings maximum profit and production with minimum input and optimal use of resources. One of the interesting applications is based on the global positioning system (GPS) and GIS technologies that help calculate optimal paths for tractors. With Machine Learning Algorithms challenging trigonometry task from university is transformed into simple solution and real money that wasnt spent on extra fuel.

4) Data collection and social network communication

The key to this is the creation of an efficient chain with local food production systems and livestock systems. This approach will create a greater understanding of the entire food supply chain efficiency and the integration of these two systems will generate long-term positive environmental impact and will deliver greater food security.

5) Computer vision

Image analysis and detection are among the most intensively growing fields in informatics research. All automated machines start from sensing, most often using cameras to obtain data that provides information about the crop and location of the harvesting system. Typically, this is an RGB camera, depth camera, or lidar system. Images are passed to machine learning pipelines that are based on effective classification approaches, including support vector machines(SVMs), neural networks, k-means, principal component analysis (PCA), feature extraction, etc. Among applications that were developed by different teams, the following could be mentioned:

6) Data transparency and blockchain

A blockchain is a method of encrypted data that conducts a search for every single transformation that has been applied to a target entity such as storing, linking, and recovering. The modern agricultural industry has accelerated and now uses a blockchain in the agriculture value chain because it is seen as a mechanism for optimizing different issues, such as transparency, cost-effectiveness, traceability, quality supply systems, etc. For example, the French food market Carrefour has been using blockchain solutions for the traceability of its products since 2018. The aim is to provide a QR code scan where consumers can retrieve data on the product on their mobile phones. The information available through the code includes the place and date of production, the products composition, the method of cultivation and etc.

7) Augmented Reality

This field is only at the development stage but has already demonstrated high potential in a specific field that requires 3D image resolutions. Visualization of animals, their diseases, and crops damages in order to assess and carry out treatment. Augmented Reality promises a lot in the near future, especially if it is combined with Artificial Intelligence (AI).

The importance of Machine Learning and Data Engineering in Agriculture should not be underestimated. Implementation of new techniques could definitely benefit farmers by increasing revenue and customers by saving them time.

Conclusion

The evolution of human transformations in agriculture outlines the main changes that the agricultural sector historically went through and at the same time points out problems that farmers face nowadays. In the era of computers and digital communication, farmers seek ways to increase profit, while consumers demand quality service in a short amount of time. In order to satisfy these needs, the Sigma Software group researched and developed several approaches, such as data communication, computer vision, blockchain, augmented intelligence and etc. These approaches along with other tools of Machine Learning and Data Engineering create a core outline for the agricultural sector. As a result, in order to increase revenues in the agricultural sector and create a more efficient chain supply, farmers need to rely more on machinery.

About the Author

Ihor Oleinich is Software Developer at Sigma Software Group. Sigma Software provides top-quality software development solutions and IT consulting to more than 170 customers all over the globe. Volvo, SAS, Oath Inc., Fortum, IGT (previously GTECH), Checkmarx, Formpipe Software, JLOOP, Vergence Entertainment, Collective, Genera Networks, Viaplay, and others trust us to develop their products. Our clients choose us for our timely and efficient communication, flexibility, strong desire, and ability to reach clients business goals.

Featured image: slonme

Link:
Machine Learning and Data Engineering Applications in Agriculture - TechNative