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Poor Conducts By Avast Antivirus Review: Is Avast Good? – The Daily Sound

Avast is antivirus protection for any computer system. The best thing about this software is that it is free of cost along with added features. Even though it does not cost anything, it provides excellent benefits of security. To find out more on avast antivirus review, read below!

Along with a strong password manager, avast antivirus provides a browser security and enables gaming mode. And not only does it just provide security benefits, but also you can make changes in the program according to the style of your suite. This is because avast antivirus is compatible with mostly all versions of windows.

As a part of Avast free antivirus, the Avast Safezone Secure Browser has the ability to secure your banking credentials. Avast implements the functions of add-ons in the browser so that it blocks any ad or website if the detection system recognizes potential threats.

It also enables a mode called Bank Mode which has the ability to separate the browser from the operating system so that it can prevent malware from intruding.

Another element called Wi-Fi Inspector does the function of scanning any network that is local. The purpose of doing this is to look out for passwords that are weak or if the firmware is out of date. In addition to this, you can get the SecureLine VPN free for seven days. Prior to that, you may need to pay charges of $20 every year.

As much as the security is tight in its core, there are some underlying issues that affect the overall aspect of avast free antivirus review. One of the issues is the long scanning process time and another issue deals with a very complex interface.

Another problem with avast internet security review is that it has the ability to jot down your browsing history and searches and then give it to any third party. However, the overall features of Avast antivirus have a great positive impact.

The free version of Avast antivirus is perfectly compatible with all the versions fo Windows such as Windows XP, 7/8/10 along with the versions having service packs. It gives you cover from external malware files from getting into your system.

With the help of its Scan function, Avast antivirus flushes out all the possible virus-infected files from the system. However, if youd like to avail of the functions of firewall and additional protection features then you will have to upgrade to the paid version of the anti-virus.

Avast Internet Security version has extensive features of the firewall. A function in this version provides a feature that will let you make a copy of a key if and while the system is on any attack.

In addition to that, Avast has a function algorithm that protects users from getting targetted in webcam spying by intruders. Avast also shreds files so that intruders cannot access or view confidential files. This is simply done by making the entire file disappear.

is avast worth paying for? Well, if you opt for the Premium version of the software then youd have to pay $70 every year. Avast Ultimate is another version that gives you the benefits of unlimited subscriptions for the VPN service version. This version of Avast Antivirus is known as SecureLine VPN Service.

And not only that, but you can also avail the features of system optimizing and password manager. Along with that, you will also get features related to identity protection.

If you want to avail security for a single operating system then at $120, you can Avast Ultimate version. But, at the same price, you can also get BitDefender Total Security (Family Pack) with similar protection against malware attacks.

Avast antivirus is made of defensive layers. Among the defensive layers, the first layer scans for the files and tries to compare so that it can tell if its a malware or not. The second layer of the defensive layer pays close attention to activities to check for snippets. This is done to check if there is a possible chance of malware attack or not.

If Avast recognizes anything suspicious going on, then it makes a report and sends it to the cloud lab of Avast to do some analysis. If the analysis confirms that the file is malware then a new signature of malware is distributed to approximately 400 million Avast users.

In addition to that, the free version of Avast antivirus provides add ons for browsers like Google Chrome and Mozilla Firefox. These particular add ons lets you be away from potential threats. To do that, it scans the attachments of email to check if any other software is dangerous to the system or not.

The detection engine of Avast antivirus has a shared-link with all the products of antivirus. After testing in 4 labs, the engine performance of Avast has been known to do an average decent job.

According to a lab test in Utah of avast premier review, Avast free antivirus has scored a rating of 98% in the category of blocking websites full of malicious files, in the year of 2018.

Another test report of avast reviews by a lab in Germany suggests that Avast conducts Zero-day protection. This was not seen before through the normal scanning procedure.

In the month of January and February of 2018, Avast antivirus was recognized to work fine on Windows version 7 with approximately a detection score of 100%. However, there were a total of 7 false detection reports.

In Windows 10, Avast did not perform well. In the month of March and April of 2018, Avast stopped at the percentage of 99.0 as well as 98.8 during the zero-day activity.

Another report in Windows 10 has portrayed that there has been improvement in Avast. This is because Avast was able to recognize the zero-day attack and a widespread attack of malware in the month of May in 2018. However, Avast was able to block only 92.2% of it.

Avast has scored a percentage of 99.4 over the courses of 6 months test in detecting malware that are available on the internet. Even, then Avast antivirus has gone through the turmoil of ups and downs.

According to a lab test in the lab of SE, in Britain, Avast performed poorly. In addition to the question, is avast good?, Avast Free Antivirus scored the accuracy percent rate of 91 during the evaluation in SE lab in the year of 2018. This implied that Avast came poorly in the tests while other antiviruses scored higher than that.

In the test, out of 100 attacks, Avast was unable to detect 5 of them and was not able to stop another 4 attacks. This implied that Avast could not protect the system from a total of 9 malware infections.

If youre planning to install Avast Antivirus Free version then it should take up to 6-7 minutes. Avast antivirus has over 48 languages that you can choose from. There is an option called Pirate Talk as well.

This will you whether youd like to make the Avast Safezone as a default browser or not. Also, youd be asked whether youd like to get the Avast application for your smartphone device or not.

However, during the installation process, Avast will confirm with you about the fact that it collects the browsing pattern to help its analytics. This is the part where Avast will ask for your consent which cannot be skipped during the installation unless you agree with it.

Since there is no option to avoid this, you can still go to the section called Settings Privacy. From there, you can disable the option for pop-up ads. You also do not need to set up an account as it is not necessary. But, if you do create an Avast account, you will be able to get access from a remote location through the application of Avast for smartphones.

Avast Antivirus has an extensive range of features to offer when it comes to security. As Avast is always trying to improve its quality of functions in detecting malware, there is a high chance that Avast will be of great help to users!

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Global IT Security Spending Market 2018 Check Point Software Technologies, Cisco Systems, EMC, Fortinet, Juniper Networks – The Industry Press…

The IT Security Spending Market report gives a purposeful depiction of the area by the practice for research, amalgamation, and review of data taken from various sources. The market analysts have displayed the different sidelines of the area with a point on recognizing the top players (Check Point Software Technologies, Cisco Systems, EMC, Fortinet, Juniper Networks, McAfee, Palo Alto Networks, Symantec, Trend Micro, Akamai Technologies, Avast Software, AVG Technologies, Barracuda Networks, Citrix Systems, Dell SonicWALL, F5 Networks, Hewlett-Packard, IBM, Imperva, Microsoft, Panda Security, Radware, Sophos, Trustwave Holdings) of the industry. The IT Security Spending market report correspondingly joins a predefined business market from a SWOT investigation of the real players. Thus, the data summarized out is, no matter how you look at it is, reliable and the result of expansive research.

This report mulls over IT Security Spending showcase on the classification, for instance, application, concords, innovations, income, improvement rate, import, and others (Commercial, Industrial, Military and Denfense) in the estimated time from 20192025 on a global stage. In like manner, the overall IT Security Spending market report reveals knowledge identified with the type of product, its applications, customers, prime players, and various components agreeing with the account. This first data demonstrates critical contenders and their definite picture of the general IT Security Spending market. Other than this, the report further demonstrates expected market power, challenges, and prospects in the IT Security Spending market.

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The report gives a broad explanation of the presence of the IT Security Spending market in different regions and countries. With an extensive regional analysis of the IT Security Spending market, the research analysts make an attempt to unveil hidden growth prospects available for players in different parts of the world. They accurately estimate market share, CAGR, production, consumption, price, revenue, and other crucial factors that indicate the growth of regional markets studied in the report. They also shed light on the presence of prominent players in regional markets, and how it is making a difference in the growth of the regional markets. The main objectives of the research report elaborate the overall market overview on IT Security Spending market dynamics, historic volume and value, robust market methodology, current & future trends, Porters Five Forces Analysis, upstream and downstream industry chain, new technological development, cost structure, government policies & regulations, etc.

IT Security Spending Market report segmentation on Major Product Type:Internet security, Endpoint security, Wireless security, Network security, Cloud security

The global version of this report with a geographical classification such as

North America (the United States, Canada, and Mexico)Europe (Germany, UK, France, Italy, Russia, Spain, and Benelux)Asia Pacific (China, Japan, India, Southeast Asia, and Australia)Latin America (Brazil, Argentina, and Colombia)The Middle East and Africa

Reason to buy IT Security Spending Market Report : 1) Breakdown of the sales data at the country level, with sales, revenue and market share for key countries in the world, from 2014 to 2019.2) The IT Security Spending competitive situation, sales, revenue and global market share of top manufacturers are analyzed emphatically by landscape contrast.3) Describe IT Security Spending sales channel, distributors, customers, research findings and conclusion, appendix and data source.4) The details of the competitive landscape outlined in this report are likely to provide an analysis of the prominent industry vendors, their growth profiles, strategies, and tactics, etc., that would help investors in decision-making.5) To project the size of IT Security Spending submarkets, with respect to key regions (along with their respective key countries).6) To strategically profile the key players and comprehensively analyze their growth strategies.7) Focuses on the key global IT Security Spending players, to define, describe and analyze the value, market share, market competition landscape, SWOT analysis and development plans in the next few years.

This report contributes an overall summary of the global IT Security Spending market, including business perspectives, market strategies, assembles data related to various business firms, its year of establishment, contact information, market outline, sales revenue, industry segments, the business most prestigious location, and regional presence. The report includes several plans and policies related to the IT Security Spending industry, moreover, it describes the management process, product appearance, manufacturing cost, and market volume. In addition, the global IT Security Spending market report implicates financial usage, the quantity of product, chain format, demand and supply ratio. This report justifies the various business trends followed by the marketing sectors as well as the distributors of the IT Security Spending industry.

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The next part also sheds light on the gap between supply and consumption. Apart from the mentioned information, the growth rate of the IT Security Spending market in 2023 is also explained. Finally, the possibility analysis of new project investment is done in the report, which contains a comprehensive SWOT analysis of the IT Security Spending market.

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Global IT Security Spending in Government Market 2019 by Manufacturers, Countries, Type and Application, Forecast to 2025 – The Industry Press…

The "IT Security Spending in Government Market" report contains a wide-extending factual assessment for IT Security Spending in Government, which enables the customer to separate the future complicity and estimate the right execution. The advancement rate is evaluated dependent on insightful examination that gives credible information on the worldwide IT Security Spending in Government market. Imperatives and advancement points are merged together after a significant comprehension of the improvement of the IT Security Spending in Government market. The report is all around made by considering its essential information in the overall IT Security Spending in Government market, the essential components in charge of the interest for its products and administrations. Our best analysts have surveyed the IT Security Spending in Government market report with the reference of inventories and data given by the key players (Check Point Software Technologies, Cisco Systems, Fortinet, Juniper Networks, Arbor Networks, Barracuda Networks, Dell SonicWall, F5 Networks, FireEye, Palo Alto Networks, Sophos, Trend Micro), flexible sources and records that help to upgrade cognizance of the related methodological conditions.

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The IT Security Spending in Government market report shows a point by point division (Internet security, Endpoint security, Wireless security, Network security, Cloud security) of the overall market reliant on development, product type, application, and distinctive techniques and systems. The point-to-point elucidation of the IT Security Spending in Government market's assembling system, the usage of advancement, conclusions of the world market players, dealers and shippers' order, and the explicit business data and their improvement plans would help our customers for future courses of action and movement planned to make due in the IT Security Spending in Government market.

The IT Security Spending in Government market report includes the latest mechanical enhancements and new releases to engage our customers to the configuration, settle on taught business decisions, and complete their required executions in the future. The IT Security Spending in Government market report moreover focuses more on current business and present-day headways, future methodology changes, and open entryways for the IT Security Spending in Government market. Nearby progression frameworks and projections are one of the key segments that clear up overall execution and incorporate key geological analysis (Government, Military, Financial Authority, Police).

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The overall IT Security Spending in Government market is made with the fundamental and direct conclusion to exploit the IT Security Spending in Government market and participate in business progression for imperative business openings. The correct figures and the graphical depiction of the IT Security Spending in Government market are shown in a represented strategy. The report shows an examination of conceivable contention, current market designs and other basic characteristics all around the world.

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The Bot Decade: How AI Took Over Our Lives in the 2010s – Popular Mechanics

Bots are a lot like humans: Some are cute. Some are ugly. Some are harmless. Some are menacing. Some are friendly. Some are annoying ... and a little racist. Bots serve their creators and society as helpers, spies, educators, servants, lab technicians, and artists. Sometimes, they save lives. Occasionally, they destroy them.

In the 2010s, automation got better, cheaper, and way less avoidable. Its still mysterious, but no longer foreign; the most Extremely Online among us interact with dozens of AIs throughout the day. That means driving directions are more reliable, instant translations are almost good enough, and everyone gets to be an adequate portrait photographer, all powered by artificial intelligence. On the other hand, each of us now sees a personalized version of the world that is curated by an AI to maximize engagement with the platform. And by now, everyone from fruit pickers to hedge fund managers has suffered through headlines about being replaced.

Humans and tech have always coexisted and coevolved, but this decade brought us closer togetherand closer to the futurethan ever. These days, you dont have to be an engineer to participate in AI projects; in fact, you have no choice but to help, as youre constantly offering your digital behavior to train AIs.

So heres how we changed our bots this decade, how they changed us, and where our strange relationship is going as we enter the 2020s.

All those little operational tweaks in our day come courtesy of a specific scientific approach to AI called machine learning, one of the most popular techniques for AI projects this decade. Thats when AI is tasked not only with finding the answers to questions about data sets, but with finding the questions themselves; successful deep learning applications require vast amounts of data and the time and computational power to self-test over and over again.

Deep learning, a subset of machine learning, uses neural networks to extract its own rules and adjust them until it can return the right results; other machine learning techniques might use Bayesian networks, vector maps, or evolutionary algorithms to achieve the same goal.

In January, Technology Reviews Karen Hao released an exhaustive analysis of recent papers in AI that concluded that machine learning was one of the defining features of AI research this decade. Machine learning has enabled near-human and even superhuman abilities in transcribing speech from voice, recognizing emotions from audio or video recordings, as well as forging handwriting or video, Hao wrote. Domestic spying is now a lucrative application for AI technologies, thanks to this powerful new development.

Haos report suggests that the age of deep learning is finally drawing to a close, but the next big thing may have already arrived. Reinforcement learning, like generative adversarial networks (GANs), pits neural nets against one another by having one evaluate the work of the other and distribute rewards and punishments accordinglynot unlike the way dogs and babies learn about the world.

The future of AI could be in structured learning. Just as young humans are thought to learn their first languages by processing data input from fluent caretakers with their internal language grammar, computers can also be taught how to teach themselves a taskespecially if the task is to imitate a human in some capacity.

This decade, artificial intelligence went from being employed chiefly as an academic subject or science fiction trope to an unobtrusive (though occasionally malicious) everyday companion. AIs have been around in some form since the 1500s or the 1980s, depending on your definition. The first search indexing algorithm was AltaVista in 1995, but it wasnt until 2010 that Google quietly introduced personalized search results for all customers and all searches. What was once background chatter from eager engineers has now become an inescapable part of daily life.

One function after another has been turned over to AI jurisdiction, with huge variations in efficacy and consumer response. The prevailing profit model for most of these consumer-facing applications, like social media platforms and map functions, is for users to trade their personal data for minor convenience upgrades, which are achieved through a combination of technical power, data access, and rapid worker disenfranchisement as increasingly complex service jobs are doubled up, automated away, or taken over by AI workers.

The Harvard social scientist Shoshana Zuboff explained the impact of these technologies on the economy with the term surveillance capitalism. This new economic system, she wrote, unilaterally claims human experience as free raw material for translation into behavioural data, in a bid to make profit from informed gambling based on predicted human behavior.

Were already using machine learning to make subjective decisionseven ones that have life-altering consequences. Medical applications are only some of the least controversial uses of artificial intelligence; by the end of the decade, AIs were locating stranded victims of Hurricane Maria, controlling the German power grid, and killing civilians in Pakistan.

The sheer scope of these AI-controlled decision systems is why automation has the potential to transform society on a structural level. In 2012, techno-socialist Zeynep Tufekci pointed out the presence on the Obama reelection campaign of an unprecedented number of data analysts and social scientists, bringing the traditional confluence of marketing and politics into a new age.

Intelligence that relies on data from an unjust world suffers from the principle of garbage in, garbage out, futurist Cory Doctorow observed in a recent blog post. Diverse perspectives on the design team would help, Doctorow wrote, but when it comes to certain technology, there might be no safe way to deploy:

It doesnt help that data collection for image-based AI has so far taken advantage of the most vulnerable populations first. The Facial Recognition Verification Testing Program is the industry standard for testing the accuracy of facial recognition tech; passing the program is imperative for new FR startups seeking funding.

But the datasets of human faces that the program uses are sourced, according to a report from March, from images of U.S. visa applicants, arrested people who have since died, and children exploited by child pornography. The report found that the majority of data subjects were people who had been arrested on suspicion of criminal activity. None of the millions of faces in the programs data sets belonged to people who had consented to this use of their data.

State-level efforts to regulate AI finally emerged this decade, with some success. The European Unions General Data Protection Regulation (GDPR), enforceable from 2018, limits the legal uses of valuable AI training datasets by defining the rights of the data subject (read: us); the GDPR also prohibits the black box model for machine learning applications, requiring both transparency and accountability on how data are stored and used. At the end of the decade, Google showed the class how not to regulate when they built, and then scrapped, an external AI ethics panel a week later, feigning shock at all the negative reception.

Even attempted regulation is a good sign. It means were looking at AI for what it is: not a new life form that competes for resources, but as a formidable weapon. Technological tools are most dangerous in the hands of malicious actors who already hold significant power; you can always hire more programmers. During the long campaign for the 2016 U.S. presidential election, the Putin-backed IRA Twitter botnet campaignsessentially, teams of semi-supervised bot accounts that spread disinformation on purpose and learn from real propagandainfiltrated the very mechanics of American democracy.

Keeping up with AI capacities as they grow will be a massive undertaking. Things could still get much, much worse before they get better; authoritarian governments around the world have a tendency to use technology to further consolidate power and resist regulation.

Tech capabilities have long since proved too fast for traditional human lawmakers, but one hint of what the next decade might hold comes from AIs themselves, who are beginning to be deployed as weapons against the exact type of disinformation other AIs help to create and spread. There now exists, for example, a neural net devoted explicitly to the task of identifying neural net disinformation campaigns on Twitter. The neural nets name is Grover, and its really good at this.

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What Veterans Affairs Aims to Accomplish Through Its Artificial Intelligence Institute – Nextgov

The Veterans Affairs Department recently launched a National Artificial Intelligence Institute to coordinate and advance strategic vet-focused research and development efforts to harness the budding technology.

VA has a unique opportunity to be a leader in artificial intelligence, Secretary Robert Wilkie said in a statement. VAs artificial intelligence institute will usher in new capabilities and opportunities that will improve health outcomes for our nations heroes.

Home to Americas largest integrated health care system, the VA trains more doctors and nurses than any other entity in the nation and also houses the largest genomic knowledge base linked to health care information in the world. Throughout 2019, the agency unveiled a variety of deliberate investments and projects to leverage artificial intelligence to better meet veterans needs. For example, the agency and tech giant IBM launched an AI-powered mental fitness app to help veterans transitioning to civilian life earlier this year, and VA collaborated with DeepMind Health to develop an AI system that can forecast a life-threatening kidney disease before it appears.

The agency also appointed Dr. Gil Alterovitz as its first-ever national artificial intelligence director this summer. A Harvard Medical professor who has led national and international collaborative initiatives that used data and technology to innovate across the health care landscape, Alterovitz will serve as the NAIIs director and oversee all of its efforts. He told Nextgov Monday that the new institute has been several months in the making and will garner some federal funding for its efforts. Alterovitz also confirmed that the institute will be housed directly at the VA.

There is a special opportunity to work for veteran needs via AI by focusing on improving health and well-being [through research and development], he said. We hope to focus on veteran priorities in such work.

NAII will engage veterans and stakeholders across the health care sector to solicit and execute flagship AI research projects that emphasize topics like deep learning, explainable AI, and privacy-preserving AI. Theyll aim to demonstrate [the] size, scope, and magnitude of capabilities that deliver positive real-world outcomes for Veterans. According to agency insiders, one of the first tasks the NAII took on was surveying the existing use of AI by VA researchers and going forward, the institute will also boost AI-related research projects already underway by offering up fresh resources and forging new possibilities for collaboration.

Medical centers are across the country and new insights can be best done working together, Alterovitz said.

The AI director also has extensive experience leading projects known as tech sprints, which essentially enable outside organizations to test out data in the VA format to develop tools and programs that can lead to new data-driven insightswithout waiting long periods to establish partnership agreements. NAII insiders will lead AI tech sprints to accelerate innovation in the ecosystem and also aim to create an AI Tech Sprint handbook to help new teams orchestrate sprints to introduce health care solutions.

"We envision a future where AI can give us tools to serve Veterans in the best way possible, as they did for our nation," Alterovitz said.

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Artificial Intelligence (AI) in Supply Chain Market Worth $21.8 billion by 2027- Exclusive Report by Meticulous Research – GlobeNewswire

London, Dec. 10, 2019 (GLOBE NEWSWIRE) -- According to a new market research report Artificial Intelligence in Supply Chain Market by Component (Platforms, Solutions), Technology (Machine Learning, Computer Vision, Natural Language Processing), Application (Warehouse, Fleet, Inventory Management), & End User - Global Forecast to 2027, published by Meticulous Research, the AI in Supply Chain Market is expected to grow at a CAGR of 39.4% from 2019 to reach $21.8 billion by 2027.

Today supply chain networks are becoming more and more complex owing to progressive globalization. Various well-established supply chain organizations across the globe are increasingly struggling with rising cost of operations, dissatisfied customers, declining sales, and unidentified competition. Therefore, the adoption of artificial intelligence technologies in supply chain operations is on the rise in order to create new opportunities & enhance operational capabilities by leveraging new possibilities, fastening processes, and making organizations adaptable to changes in the future.

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Realizing the fact, various end-use industries are investing heavily in order to reap the profits in highly dynamic and competitive market environments. Organizations are aggressively adopting AI-based solutions for supply chain operations to reshape their business processes and increase profitability. Rapid adoption of AI technology across the supply chain operations, rising awareness about artificial intelligence, and widening implementation of computer vision technologies across several end-use industries are the key factors driving steady growth in the global artificial intelligence in supply chain market.

In recent years, the funding for development and implementation of artificial intelligence solutions for supply chain industry has increased significantly. For instance, in 2018, the Government of Qubec invested $60 million in order to support AI-Powered Supply Chains Supercluster (SCALE.AI). Similar investments were also made by the Government of Canada investing up to nearly $230 million for the AI-Powered Supply Chains Supercluster in 2018. Such initiatives are bringing the manufacturing, retail, and information & communications technology sectors on the same platform, to develop intelligent solutions for supply chain management through incorporation of robotics and AI technologies.

The AI in supply chain market study presents historical market data in terms of value (2017 and 2018), estimated current data (2019), and forecasts for 2027 by component, technology, application, end-user, and geography. The study also evaluates industry competitors and analyzes their market share at the global and regional levels.

Based on component, the software segment is estimated to account for the largest share of the overall artificial intelligence in supply chain market in 2019; and is slated to grow at the fastest CAGR during the forecast period. The large share of this segment is attributed to the supply chain visibility offered by software, including inventory control, warehouse management, order procurement, and reverse logistics and tracking.

Based on technology, the machine learning segment is estimated to account for the largest share of the overall AI in supply chain market, in 2019. This is mainly attributed to the growing demand for AI-based intelligent solutions, increasing government initiatives, and ability of AI solutions to efficiently handle and analyze big data and quickly scan, parse, and react to anomalies. On the other hand, computer vision technology is slated to grow at the fastest CAGR during the forecast period, due to widening implementation of computer vision across several end-use industries for monitoring operations, spotting suspicious behavior, and preventing thefts.

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Based on the application, supply chain planning is estimated to hold the largest share of the overall AI market in supply chain, in 2019. This is mainly attributed to the ability of AI solutions to optimize supply chain operations and digitize existing processes and workflows by reinventing the supply chain planning. On the other hand, the demand for AI solutions for warehouse management applications is slated to grow at a fastest CAGR during the forecast period, mainly due to benefits offered by AI solutions in the form of optimizing the logistics, spotting & detecting abnormalities, and automated sorting.

Based on end-user, the consumer-packaged-goods (CPG) segment is estimated to hold the largest share of the overall artificial intelligence in supply chain market in 2019, due to expanding e-commerce sector and ability of AI solutions to provide profitable drop-shipping with features like product tracking, inventory management, and warehouse management. On the other hand, the retail segment is slated to grow at the fastest CAGR during the forecast period, mainly due to benefits of AI in the form of addressing issues with stocking inefficiencies, complexity of operations, and high product lead times in supply chain operations of the retail industry.

The report also includes an extensive assessment of the key strategic developments adopted by leading market participants in the AI in supply chain industry over the past 4 years (2016-2019). The artificial intelligence in supply chain market has witnessed number of partnerships & agreements in the recent years. For instance, in December 2018, Google announced a strategic partnership with Iguazio to provide real-time supply chain and inventory management services for the retail sector.

The global artificial intelligence in supply chain market is highly fragmented with the presence of key players, such asIntel Corporation (U.S.),, Inc. (U.S.), Google LLC (U.S.), Microsoft Corporation (U.S.), Nvidia Corporation (U.S.), Oracle Corporation (U.S.), IBM Corporation (U.S.), Samsung (South Korea), LLamasoft Inc. (U.S.), SAP (Germany), General Electric (U.S.), Deutsche Post AG DHL (Germany), Xilinx (U.S.), Micron Technology, Inc. (U.S.), FedEx (U.S.), and ClearMetal, Inc. (U.S.) along with several local and regional players.

Browse key industry insights spread across 180 pages with 167 market data tables & 29 figures & charts from the market research report:

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AI in Supply Chain Market, by Component

AI in Supply Chain Market, by Technology

AI in Supply Chain Market, by Application

AI in Supply Chain Market, by End User

AI in Supply Chain Market, by Geography

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About Meticulous Research

The name of our company defines our services, strengths, and values. Since the inception, we have only thrived to research, analyze and present the critical market data with great attention to details.

Meticulous Research was founded in 2010 and incorporated as Meticulous Market Research Pvt. Ltd. in 2013 as a private limited company under the Companies Act, 1956. Since its incorporation, with the help of its unique research methodologies, the company has become the leading provider of premium market intelligence in North America, Europe, Asia-Pacific, Latin America, and Middle East & Africa regions.

With the meticulous primary and secondary research techniques, we have built strong capabilities in data collection, interpretation, and analysis of data including qualitative and quantitative research with the finest team of analysts. We design our meticulously analyzed intelligent and value-driven syndicate market research reports, custom studies, quick turnaround research, and consulting solutions to address business challenges of sustainable growth.

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Artificial Intelligence (AI) in Supply Chain Market Worth $21.8 billion by 2027- Exclusive Report by Meticulous Research - GlobeNewswire

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Finland seeks to teach 1% of Europeans basics on artificial intelligence – Reuters UK

TALLINN (Reuters) - Finland, which holds the rotating EU presidency until the end of the year, said on Tuesday it aims to teach 1% of all Europeans basic skills in artificial intelligence through a free online course it will now translate into all official EU languages.

The European Union is pushing for wide deployment of artificial intelligence across the bloc, to help European companies catch up with rivals in Asia and the United States.

Our investment has three goals: we want to equip EU citizens with digital skills for the future, we wish to increase practical understanding of what artificial intelligence is, and by doing so, we want to give a boost to the digital leadership of Europe, said Finnish Minister of Employment Timo Harakka.

As our Presidency ends, we want to offer something concrete. Its about one of the most pressing challenges facing Europe and Finland today: how to develop our digital literacy, Harakka said in a statement.

The course, conducted by the University of Helsinki and originally launched in 2018, already has enrolled more than 220,000 students from more than 110 countries.

It includes modules on subjects such as machine learning, neural networks, the philosophy of artificial intelligence and using artificial intelligence to solve problems.

The course is available in English, Finnish, Swedish and Estonian so far, and Finland will translate it to all official EU languages next year.

The original goal to educate 1% of Finns, equalling some 55,000 people, was reached in just a few months.

Reporting by Tarmo Virki, editing by Anne Kauranen

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Baidu Leads the Way in Innovation with 5,712 Artificial Intelligence Patent Applications – MarTech Series

Baidu, Inc. has filed the most AI-related patent applications in China, a recognition of the companys long-term commitment to driving technological advancement, a recent study from the research unit of Chinas Ministry of Industry and Information Technology (MIIT) has shown.

Baidu filed a total of 5,712 AI-related patent applications as of October 2019, ranking No.1 in China for the second consecutive year. Baidus patent applications were followed by Tencent (4,115), Microsoft (3,978), Inspur (3,755), and Huawei (3,656), according to the report issued by the China Industrial Control Systems Cyber Emergency Response Team, a research unit under the MIIT.

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Baidu retained the top spot for AI patent applications in China because of our continuous research and investment in developing AI, as well as our strategic focus on patents, said Victor Liang, Vice President and General Counsel of Baidu.

In the future, we will continue to increase our investments into securing AI patents, especially for high-value and high-quality patents, to provide a solid foundation for Baidus AI business and for our development of world-leading technology, he said.

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The report showed that Baidu is the patent application leader in several key areas of AI. These include deep learning (1,429), natural language processing (938), and speech recognition (933). Baidu also leads in the highly competitive area of intelligent driving, with 1,237 patent applications, a figure that surpasses leading Chinese universities and research institutions, as well as many international automotive companies. With the launch of the Apollo open source autonomous driving platform and other intelligent driving innovations, Baidu has been committed to pioneering the intelligent transformation of the mobility industry.

After years of research, Baidu has developed a comprehensive AI ecosystem and is now at the forefront of the global AI industry. Moving forward, Baidu will continue to conduct research in the core areas of AI, contribute to scientific and technological innovation in China, and actively push forward the application of AI into more vertical industries. Baidu is positioned to be a global leader in a wave of innovation that will transform industries.

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Baidu Leads the Way in Innovation with 5,712 Artificial Intelligence Patent Applications - MarTech Series

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Artificial Intelligence as Security Solution and Weaponization by Hackers – CISO MAG

By Julien Legrand, Operation Security Manager, Socit Gnrale

Artificial intelligence is a double-edged sword that can be used as a security solution or as a weapon by hackers. AI entails developing programs and systems capable of exhibiting traits associated with human behaviors. The characteristics include the ability to adapt to a particular environment or to intelligently respond to a situation. AI technologies have extensively been applied in cybersecurity solutions, but hackers are also leveraging them to develop intelligent malware programs and execute stealth attacks.

Security experts have conducted a lot of research to harness the capabilities of AI and incorporate it into security solutions. AI-enabled security tools and products can detect and respond to cybersecurity incidents with minimal or zero input from humans. AI applications in cybersecurity have proved to be highly useful. Twenty-five percent of IT decision-makers attribute security as the primary reason why they adopt AI and machine learning in organizational cybersecurity. AI not only improves security posture, but it also automates detection and response processes. This cuts on the finances and time used in human-driven intervention and detection processes.

Organizations use AI to model and monitor the behavior of system users. The purpose of monitoring the interactions between a system and users is to identify takeover attacks. These are attacks where malicious employees steal login details of other users and use their accounts to commit different types of cybercrimes. AI learns the user activities over time such that it considers unusual behavior as anomalies. Whenever a different user uses the account, AI-powered systems can detect the unusual activity patterns and respond either by locking out the user or immediately alert system admins of the changes.

Antivirus tools with AI capabilities detect network or system anomalies by identifying programs exhibiting unusual behavior. Malware programs are coded to execute functions that differ from standard computer operations. AI antiviruses leverage machine learning tactics to learn how legitimate programs interact with an operating system. As such, whenever malware programs are introduced to a network, AI antivirus solutions can immediately detect them and block them from accessing systems resources. This contrasts from signature-based traditional antiviruses which scans a signature database to determine whether a program is a security threat.

Automated analysis of system or network data ensures continuous monitoring for prompt identification of attempted intrusions. Manual analysis is nearly impossible due to the sheer volume of data generated by user activities. Cybercriminals use command and control (C2) tactics to penetrate network defenses without being detected. Such tactics include embedding data in DNS requests to bypass firewalls and IDS/IPS. AI-enabled cyber defenses utilize anomaly detection, keyword matching, and monitoring statistics. As a result, they can detect all types of network or system intrusion.

Cybercriminals prefer email communication as the primary delivery technique for malicious links and attachments used to conduct phishing attacks. Symantec states that 54.6 percent of received email messages are spam and may contain malicious attachments or links. Anti-phishing emails with AI and machine learning capabilities are highly effective in identifying phishing emails. This is by performing in-depth inspections on links. Additionally, such anti-phishing tools simulate clicks on sent links to detect phishing signs. They also apply anomaly detection techniques to identify suspicious activities in all features of the sender. These include attachments, links, message bodies, among other items.

Hackers are turning to AI and using it to weaponize malware and attacks to counter the advancements made in cybersecurity solutions. For instance, criminals use AI to conceal malicious codes in benign applications.They program the codes to execute at a specific time, say ten months after the applications have been installed, or when a targeted number of users have subscribed to the applications. This is to maximize the impacts such attacks will cause. Concealing such codes and information requires the application of AI models and deriving private keys to control the place and time the malware will execute.

Notwithstanding, hackers can predefine an application feature as an AI trigger for executing cyber-attacks. The features can range from authenticating processes through voice or visual recognition to identity management features. Most applications used today contain such features, and this provides attackers with ample opportunities of feeding weaponized AI models, deriving a key, and attacking at will. The malicious models can be present for years without detection as hackers wait to strike when applications are most vulnerable.

Besides, AI technologies are unique in that they acquire knowledge and intelligence to adapt accordingly. Hackers are aware of these capabilities and leverage them to model adaptable attacks and create intelligent malware programs. Therefore, during attacks, the programs can collect knowledge of what prevented the attacks from being successful and retain what proved to be useful. AI-based attacks may not succeed in a first attempt, but adaptability abilities can enable hackers to succeed in subsequent attacks. Security communities thus need to gain in-depth knowledge of the techniques used to develop AI-powered attacks to create effective mitigations and controls.

Also, cyber adversaries use AI to execute intelligent attacks that self-propagate over a system or network. Smart malware can exploit unmitigated vulnerabilities leading to an increased likelihood of fully compromised targets. If an intelligent attack comes across a patched vulnerability, it immediately adapts to try compromising a system through different types of attacks.

Lastly, hackers use AI technologies to create malware capable of mimicking trusted system components. This is to improve stealth attacks. For example, cyber actors use AI-enabled malware programs to automatically learn the computation environment of an organization, patch update lifecycle, preferred communication protocols, and when the systems are least protected. Subsequently, hackers can execute undetectable attacks as they blend with an organizations security environment. For example, TaskRabbit was hacked compromising 3.75 million users, yet investigations could not trace the attack. Stealth attacks are dangerous since hackers can penetrate and leave a system at will. AI facilitates such attacks, and the technology will only lead to the creation of faster and more intelligent attacks.

Disclaimer: CISO MAG does not endorse any of the claims made by the writer. The facts, opinions, and language in the article do not reflect the views of CISO MAG and CISO MAG does not assume any responsibility or liability for the same. Views expressed in this article are personal.

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Artificial Intelligence as Security Solution and Weaponization by Hackers - CISO MAG

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