Category Archives: Artificial Intelligence

Browse in-depth TOC on"AI in healthcare Market"

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The services segment is projected to foresee highest CAGR during the forecast period

AI is a complex method as it requires the implementation of sophisticated algorithms for a wide range of applications in patient data and risk analysis, lifestyle management and monitoring, precision medicine, inpatient care and hospital management, medical imaging and diagnostics, drug discovery, and virtual assistants, among others. Hence, for the successful deployment of AI, there is a need for deployment and integration, and support and maintenance services. Big technology companies such as Microsoft (US), and Google (US) are providing cloud services for AI in healthcare applications.

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Machine learning technology to hold largest size of AIin healthcare market during the forecast period

ML is being implemented in healthcare to deal with large volumes of data, where the time previously dedicated to poring over charts and spreadsheets is now being used to seek intelligent ways to automate data analysis. It is used to streamline administrative processes in hospitals, map and treat infectious diseases, and personalize medical treatments. Machine learning includes various technologies, such as deep learning, supervised learning, unsupervised learning, and reinforcement learning.

The key players operating in the artificial intelligence in healthcare market

Europe region is expected to create high market opportunity in artificial intelligence in healthcare market during the forecast period.

The major factors driving the growth of the market in the region include the surging adoption of AI-based tools in R&D for drug discovery, favorable government initiatives to encourage technological developments in the field of AI and robotics, growing EMR adoption leading to the generation of large volumes of patient data, increasing venture capital funding, rising healthcare expenditure, and growing geriatric population.

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Artificial Intelligence in ManufacturingMarket by Offering (Hardware, Software, and Services), Industry, Application, Technology (Machine Learning, Natural Language Processing, Context-aware Computing, Computer Vision), & Region (2022-2027)

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Artificial Intelligence in Healthcare Market worth $67.4 billion by 2027 - Exclusive Report by MarketsandMarkets - PR Newswire UK

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Welcome to Take Five. Im Stephanie Neil and today were looking at technologies that will help with traceability and product authenticity.

While the industry has been overwhelmed by supply chain issues and skyrocketing prices, we tend to forget that there are still important regulatory mandates that many manufacturers in the food and beverage and pharmaceutical industries need to comply with.

For example, this past October, the Food and Drug Administration held a three-day summit to better understand how foods are sold through business to consumer e-commerce models and the standards of care used by industry to control the safety risks of food sold online.

This followed the introduction of the FDAs New Era of Smarter Food Safety Blueprint, which was first introduced in 2020, and identifies future paths of action needed to address new business models of food delivery.

The blueprint is centered around four core elements:

Tech-enabled traceability is the first step in the FDAs work, which includes harmonizing the key data elements and critical tracking events to provide end-to-end traceability.

And the technology to do that is blockchain.

A reminder of what blockchain isit is the technology that underpins cryptocurrencies and other applications by providing a secure, decentralized approach to distributing digital information in a way that can be shared but not modified. The FDA is encouraging more use of blockchain to help pinpoint the exact sources of foods involved in an outbreak.

It sounds like a great plan, but it will be up to the manufacturers to figure out how to implement a tracking system that includes last mile delivery to the consumer, and these companies already have a whole lot on their plate right now.

Fortunately, there are other technologies surfacing that can help. Ill tell you about one in particular that can even keep counterfeit goods out of the supply chain.

In addition to food safety, combatting counterfeit goods is another area of concern for manufacturers. The traditional approach to this has been barcodes, tags, and special labels. The problem is these things can be removed, get damaged, or be counterfeit themselves.

But a startup company called Alitheon has come up with a way to solve this problem using optical artificial intelligence. Much like a fingerprint, which is very unique, the optical AI identifies physically inherent characteristics of goods. Because, even if they appear identical, they are not.

The Alitheon algorithm captures a one-of-a-kind FeaturePrint which can identify minuscule differences in the surface of an item. That image is stored in a system to create a digital baseline for future reference. So the end user on the receiving end can simply take a picture of the item using their mobile phone and the Alitheon app will know right away if the product is authentic or has been tampered with during its travels.

There are many potential ways to use this technology. For example, this could solve recall problems. In the automotive industry, a recall requires all cars in question be brought back to the shop to be checked. Instead, a simple picture of the part would tell the mechanic when it was manufactured, saving a lot of time and money.

Thats all we have time for today, thank you for joining me on this edition of Take Five, Ill see you next time.

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Blockchain and artificial intelligence keep food, goods safe in supply chain. - Automation World

- Nagaraj Bhat, VP & MD of Cardinal Health International India, talks about whats fuelling digital transformation in the healthcare sector.

-The market for global augmented reality in healthcare is expected to grow to $1.9 billion in 2022, from $1.4 billion last year.

- By 2050, one in every four individuals in Europe and North America will be beyond the age of 65, putting more patients with complicated requirements on the healthcare system's plate.

Cardinal Health, the $165-billion global healthcare distributor and manufacturer, opened its global tech hub in Bengaluru in April this year. The launch of Cardinal Health International India (CHII) as a new global capability centre, according to the company, is a step towards providing more innovative healthcare solutions and enabling digital transformation.

Cardinal Health has invested $8 million in CHII, which plans to acquire local talent and function as a global hub for digital skills and delivery. The firm currently has around 400 employees and plans to expand to over 600 by the end of this year.

CHII will also support enterprise-level digital transformation initiatives across Cardinal Health.

With its core focus on technologies such as artificial intelligence (AI), automation and analytics, CHII has been instrumental in helping Cardinal Health develop robust healthcare systems in terms of delivery, outcomes and reduced costs.

Is there a talent supply and demand gap that impedes digital transformation and innovation in the healthcare sector? How can we address this?

Since the beginning of the COVID-19 pandemic, the rate of digital transformation has increased rapidly in every sector, and it is expected to continue long after it is over. As companies seek to embrace more digital ways of doing business, there is a swelling need for employees with digital and technology skill sets. Certainly, the demand for talent is higher than the supply. And the only way to work around this is by reskilling and upskilling your current employees.

If we take Cardinal Health as an example, we provide our employees access to learning programmes anytime and anywhere. We believe that easy access to learning modules helps improve employee efficiency significantly. We have used gamification methods to drive greater engagement in learning modules. The game mechanics to reward learners with points and badges, coupled with publishing leaderboards, have made learning very competitive and engaging.At CHII, we continually assess skill gaps through surveys and expressions of interest in various areas. This data has helped us proactively address skill gaps, hence encouraging a culture of continuous learning. The disruptive challenges created during the pandemic must be converted into opportunities that require organisations to equip the existing workforce to solve.

CHIIs focus here is on the 3As - artificial intelligence, automation and analytics. Tells us how augmented intelligence will shape the healthcare sector globally. What are some of the immediate use cases of the technology?

For more details, visit@ https://www.businessinsider.in/tech/enterprise/news/artificial-intelligence-has-the-potential-to-change-healthcare/articleshow/93130054.cms

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Artificial Intelligence poised to change healthcare industry - Bio-IT World

Everyone knows the sci-fi movie cliche of an artificial intelligence program inevitably slipping down a villainous path. Thankfully, when it's not scripted by a Hollywood screenwriter, a super smart AI isn't all bad.

On Thursday, Alphabet-owned, UK-based AI firm DeepMind revealed that its AlphaFold algorithm has effectively predicted the shape of nearly every protein known to science, which could rapidly accelerate drug discovery and breakthroughs in biology. They're giving away the database to anyone for free.

The human mind is incredible, or at least Isaac Newton and Albert Einstein's were. But with all the tools available to our species' best and brightest researchers, figuring out the shape of one protein can take months or years of laboratory study. In fact, humans have only figured out the shape of about 0.01%, or 190,000, of known proteins.

Cracking the shape of proteins, which are essential for life, is critical to understanding a protein's function, and understanding the inner workings gives scientists the potential to alter its DNA sequence or identify drugs that could attach to it. In the case of malaria, for example, studying the proteins of the parasite can reveal how antibodies bind to it and open pathways to fighting it. By performing work that could have taken humans decades, DeepMind's AI gives scientists a roadmap for a new world of discovery:

"Almost every drug that has come to market over the past few years has been in part designed through knowledge of protein structures," Janet Thornton, a senior scientist at EMBL-EBI, told the Financial Times.

Manpower: Human scientists aren't out of a job, they still have to confirm the protein structures in experiments, but have been essentially handed years of work. Plus they can apply for a job at Isomorphic Labs, the UK company that Alphabet smartly set up to use DeepMind's technology to accelerate drug discovery.

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An Artificial Intelligence Predicted the Shape of Nearly Every Known Protein - The Motley Fool

MIAMI

The Texas Public Power Association Annual Meeting took place on July 25-27, 2022, in Austin, TX. This event gathered a wide range of energy industry professionals to share their knowledge and experiences related to the industrys present and future challenges. During this conference, Open International brought in two speakers to discuss how utilities can leverage artificial intelligence (AI) to improve their customer experience (CX).

Throughout the presentation, Open Internationals speakers, Juan Corredor, Opens CTO, and Felipe Corredor, Industry Consultant, showed how utilities can strengthen their CX by implementing a CIS solution enriched with artificial intelligence and business rule engine components. They demonstrated how conversational tools work with a modern CIS and how utilities can exceed their customers expectations. In this session, we wanted to show how with Artificial Intelligence (AI) in their toolbox, utilities can provide their customers with delightful experiences powered by modern technologies and software applications, particularly, chatbots, smart speakers, and smart workflows Felipe Corredor said.

Commenting on the conference and Opens presentation, Juan Corredor stated, At Open, weve been working on creating a new simple way to enable utilities to interact with their customers in an efficient and personalized manner, allowing them to anticipate their customers needs through artificial intelligence and data analytics. We enjoyed sharing our industry knowledge and solution with everyone at the TPPA Annual Meeting.

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Since its inception in 1987, Open International has provided technology that helps Telecommunications and Utility service providers meet their business goals and implementinnovativebusiness strategies. Opens software solution has allowed our clients to stay on top of their industrys biggest challenges by giving them the agility to act on current-day and future problems. We believe that through truly great technology, we can help simplify the way service providers operate, create value, and increase customer satisfaction. With these core values, we created our single, state-of-the-art, comprehensive product:Open Smartflexis a holistic, multi-service, preconfigured software solution that provides a powerful billing engine, a robust customer care suite, an agile mobile workforce management system, a smart metering engine and hundreds of other functionalities to satisfy our clients core needs.

http://www.openintl.com

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Open International revealed how to strengthen your utility's CX with Artificial Intelligence at the TPPA 2022 Annual Meeting - Utility Dive

The MIT alumni-founded Overjet uses artificial intelligence to annotate dental X-rays for dentists. Credit: Courtesy of Overjet

MIT alumni-founded company analyzes and annotates dental X-rays to help dentists offer more comprehensive care.

A hospital radiologist is often pictured as a specialist who sits in a dark room and spends hours poring over X-rays to make diagnoses. Contrast that with your dentist, who in addition to interpreting X-rays must also perform surgery, communicate with patients, manage staff, and run their business. When dentists analyze X-rays, they generally do so in bright rooms and on computers that arent specialized for radiology, often with the patient sitting right next to them.

It shouldnt come as a surprise, then, that dentists given the same X-ray might propose different treatments.

Dentists are doing a great job given all the things they have to deal with, says Wardah Inam SM 13, PhD 16.

Inam is the co-founder of Overjet, an MIT alumni-founded company that uses artificial intelligence to analyze and annotate X-rays for dentists and insurance providers. Overjets goal is to take the subjectivity out of X-ray interpretations to improve patient care.

Its about moving toward more precision medicine, where we have the right treatments at the right time, says Inam, who co-founded the company with Alexander Jelicich 13. Thats where technology can help. Once we quantify the disease, we can make it very easy to recommend the right treatment.

Overjet has been cleared by the Food and Drug Administration (FDA) to detect and outline cavities and quantify bone levels to aid in the diagnosis of periodontal disease, a common but preventable gum infection that causes the jawbone and other tissues supporting the teeth to deteriorate.

Overjets software analyzes and annotates dental X-rays automatically in near real-time, offering information on the type of X-ray taken, how a tooth may be impacted, the exact level of bone loss with color overlays, the location and severity of cavities, and more. Credit: Courtesy of Overjet

Besides helping dentists detect and treat diseases, Overjets software is also designed to help dentists show patients the problems theyre seeing and explain why theyre recommending certain treatments.

The company has already analyzed tens of millions of X-rays. They are used by dental practices nationwide and are currently working with insurance companies that represent more than 75 million patients in the U.S. Inam is hoping the data Overjet is analyzing can be used to further streamline operations while improving care for patients.

Our mission at Overjet is to improve oral health by creating a future that is clinically precise, efficient, and patient-centric, says Inam.

Its been a whirlwind journey for Inam, who knew nothing about the dental industry until her interest was piqued after a bad experience in 2018.

Inam came to MIT in 2010, first for her masters and then her PhD in electrical engineering and computer science, and says she caught the bug for entrepreneurship early on.

For me, MIT was a sandbox where you could learn different things and find out what you like and what you dont like, Inam says. Plus, if you are curious about a problem, you can really dive into it.

While taking entrepreneurship classes at the Sloan School of Management, Inam eventually started a number of new ventures with classmates.

I didnt know I wanted to start a company when I came to MIT, Inam says. I knew I wanted to solve important problems. I went through this journey of deciding between academia and industry, but I like to see things happen faster and I like to make an impact in my lifetime, and thats what drew me to entrepreneurship.

During her postdoc in the Computer Science and Artificial Intelligence Laboratory (CSAIL), Inam and a group of researchers applied machine learning to wireless signals to create biomedical sensors that could track a persons movements, detect falls, and monitor respiratory rate.

She didnt get interested in dentistry until after leaving MIT, when she changed dentists and received an entirely new treatment plan. Confused by the change, she asked for her X-rays and asked other dentists to have a look, only to receive still another variation in diagnosis and treatment recommendations.

At that point, Inam decided to dive into dentistry for herself, reading books on the subject, watching YouTube videos, and eventually interviewing dentists. Before she knew it, she was spending more time learning about dentistry than she was at her job.

The same week Inam quit her job, she learned about MITs Hacking Medicine competition and decided to participate. Thats where she started building her team and getting connections. Overjets first funding came from the Media Lab-affiliated investment group the E14 Fund.

The E14 fund wrote the first check, and I dont think we wouldve existed if it wasnt for them taking a chance on us, she says.

Inam learned that a big reason for variation in treatment recommendations among dentists is the sheer number of potential treatment options for each disease. A cavity, for instance, can be treated with a filling, a crown, a root canal, a bridge, and more.

When it comes to periodontal disease, dentists must make millimeter-level assessments to determine disease severity and progression. The extent and progression of the disease determines the best treatment.

I felt technology could play a big role in not only enhancing the diagnosis but also to communicate with the patients more effectively so they understand and dont have to go through the confusing process I did of wondering whos right, Inam says.

Overjet began as a tool to help insurance companies streamline dental claims before the company began integrating its tool directly into dentists offices. Every day, some of the largest dental organizations nationwide are using Overjet, including Guardian Insurance, Delta Dental, Dental Care Alliance, and Jefferson Dental and Orthodontics.

Today, as a dental X-ray is imported into a computer, Overjets software analyzes and annotates the images automatically. By the time the image appears on the computer screen, it has information on the type of X-ray taken, how a tooth may be impacted, the exact level of bone loss with color overlays, the location and severity of cavities, and more.

The analysis gives dentists more information to talk to patients about treatment options.

Now the dentist or hygienist just has to synthesize that information, and they use the software to communicate with you, Inam says. So, theyll show you the X-rays with Overjets annotations and say, You have 4 millimeters of bone loss, its in red, thats higher than the 3 millimeters you had last time you came, so Im recommending this treatment.

Overjet also incorporates historical information about each patient, tracking bone loss on every tooth and helping dentists detect cases where disease is progressing more quickly.

Weve seen cases where a cancer patient with dry mouth goes from nothing to something extremely bad in six months between visits, so those patients should probably come to the dentist more often, Inam says. Its all about using data to change how we practice care, think about plans, and offer services to different types of patients.

Overjets FDA clearances account for two highly prevalent diseases. They also put the company in a position to conduct industry-level analysis and help dental practices compare themselves to peers.

We use the same tech to help practices understand clinical performance and improve operations, Inam says. We can look at every patient at every practice and identify how practices can use the software to improve the care theyre providing.

Moving forward, Inam sees Overjet playing an integral role in virtually every aspect of dental operations.

These radiographs have been digitized for a while, but theyve never been utilized because the computers couldnt read them, Inam says. Overjet is turning unstructured data into data that we can analyze. Right now, were building the basic infrastructure. Eventually, we want to grow the platform to improve any service the practice can provide, basically becoming the operating system of the practice to help providers do their job more effectively.

Read more:

Artificial Intelligence Takes the Guesswork Out of Dental Care - SciTechDaily

DUBLIN--(BUSINESS WIRE)--The "Brazil Artificial Intelligence in Commercial Airline Market: Prospects, Trends Analysis, Market Size and Forecasts up to 2027" report has been added to ResearchAndMarkets.com's offering.

The country research report on Brazil artificial intelligence in commercial airline market is a customer intelligence and competitive study of the Brazil market. Moreover, the report provides deep insights into demand forecasts, market trends, and, micro and macro indicators in the Brazil market.

Also, factors that are driving and restraining the artificial intelligence in commercial airline market are highlighted in the study. This is an in-depth business intelligence report based on qualitative and quantitative parameters of the market. Additionally, this report provides readers with market insights and detailed analysis of market segments to possible micro levels. The companies and dealers/distributors profiled in the report include manufacturers & suppliers of artificial intelligence in commercial airline market in Brazil.

Segments Covered

Segmentation Based on Offering

Segmentation Based on Solution

Segmentation Based on Technology

Highlights of the Report

The report provides detailed insights into:

1) Demand and supply conditions of artificial intelligence in commercial airline market

2) Factor affecting the artificial intelligence in commercial airline market in the short run and the long run

3) The dynamics including drivers, restraints, opportunities, political, socioeconomic factors, and technological factors

4) Key trends and future prospects

5) Leading companies operating in artificial intelligence in commercial airline market and their competitive position in Brazil

6) The dealers/distributors profiles provide basic information of top 10 dealers & distributors operating in (Brazil) artificial intelligence in commercial airline market

7) Matrix: to position the product types

8) Market estimates up to 2027

Key Topics Covered:

1. Report Overview

2. Executive Summary

3. Market Overview

4. Brazil Artificial Intelligence in Commercial Airline Market by Offering

5. Brazil Artificial Intelligence in Commercial Airline Market by Solution

6. Brazil Artificial Intelligence in Commercial Airline Market by Technology

7. Company Profiles

For more information about this report visit https://www.researchandmarkets.com/r/evnpvr

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Brazil Artificial Intelligence in Commercial Airline Market Report 2022: Key Trends, Players and Drivers - ResearchAndMarkets.com - Business Wire

Customer feedback is important to any business but in delivery, most of the customer interaction is so limited.With a fairly large percentage of diners discovering new restaurants via social media platforms and online aggregators, and through peer reviews, reputation management thus has become an important aspect of any restaurant operation.

When customers are not happy they are on the lookout for venting out their anger, the manager would bear the brunt of the disgruntled manager then that was replaced by social media. A way to counter this is to provide a platform where the customer feels that their grievance has been heard by the management as well as how they would respond to them. This is where the feedback tool has evolved from being a mechanism to just improve services and product offerings to a mechanism in which the reputation can be managed.

The evolution of the feedback form from vocal to paper to digital now has transformed the way businesses use this tool. Most tools now come with an escalation module which ensures the right person in the organisation gets informed on customers' complaints, this ensures that important issues are scaled all the way whereas trivial issues can be managed at lower levels. Another advantage of the feedback tools is they have become omnichannel, hence not restricted to be taken using a tab. Feedbacks today collected using tablets, email, SMS and WhatsApp, Abhishek Mimani, founder of eWards (Loop) stated.

Digitisation overall has made our feedback game up by 20 to 30 percent. Digital feedback also helps the customer to choose the right products by checking the feedback of the regular customers more quickly & comfortably, Azra Golam, sales director at Aminia Restaurants commented.

Further Vishal Verma, general manager of Vijan Mahal commented that digital transformation helps to better meet customer expectations and improve operational efficiency by 40 percent, and 38 percent of executives plan to invest more in technology to make it competitive.

It plays a vital role in making the numbers or losing the numbers. People love to read reviews through social media platforms however, the conversion from assumption to reality is always by the physical visit or your own experience but it at least pushes them to visit and experience, Verma added.

Feedback can be collected via various channels: suggestion boxes, surveys, social media are some of the many possible methods available. Constant information gathering is vital in maintaining a competitive edge, as it enables businesses to tailor their services and products to shifting customer requirements. Lincoln Bennet Rodrigues, chairman and founder of The Bennet and Bernard Company feels that the quality of the service and the food enhances customer loyalty toward the brand and the business.

Sentiment analysis of the feedback data further helps to understand which pillars of the restaurant are functioning well. Maybe, the serving time is taking a hit, or the food isnt up to the mark and so on. Manually understanding customer sentiment might be quite a task. Thus tools are simplifying the process.

Since Honey and Dough have multiple locations touching more than 1500 customers a day, feedback helps the brand to notice little things that go wrong. The customers who unfortunately don't have the best experience feel heard when they are asked for their feedback. Once unsatisfactory feedback is received, our team is prompt in responding and we ensure to make it right. This definitely helps in customer retention and an increase in revenue, Utsav Chhawchharia, owner, Honey and Dough said.

According to Pratinav Pratap Singh, founder of POP THAI, feedback is great to deduce menu but customer behaviour through tech ads is a whole new world to understanding them better. I always tell people we are a tech startup maybe because I think like a scientist and believe in working around data-driven entities. Being said good food is always key here, but it's also extremely important to be tech-savvy, he commented.

He further commented that since online reviews are the only interaction with customers, you will be surprised how honest humans can be with just a few words. But said so, some customers do take their food seriously and we can see by checking their past reviews and perceive if its a cyclical rant medium for them or an honest feedback space to settle their dues.

Customer feedback is important to any business but in delivery, most of the customer interaction is so limited. We are constantly finding new ways to evolve even through technology. For instance, social media works extremely well for us, we asked followers to leave a review and offered them a completely free meal on their next order. When it comes to technology, the future step for POP THAI would be adding AI digital human technology or chatbots on our website and interacting with customers to take live orders and get their customer feedback, he said.

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The future of feedback lies in artificial intelligence - ETHospitality

AI for cybersecurity; its everywhere else!

You can use artificial intelligence (AI) to automate complex repetitive tasks much faster than a human. AI technology can sort complex, repetitive input logically. Thats why AI is used for facial recognition and self-driving cars. But this ability also paved the way for AI cybersecurity. This is especially helpful in assessing threats in complex organizations. When business structures are continually changing, admins cant identify weaknesses traditionally.

Additionally, businesses are becoming more complex in network structure. This means cybercriminals have more exploits to use against you. You can see this in highly automated manufacturing 3.0 businesses or integrated companies like the oil and gas industry. To this end, various security companies have developed AI cybersecurity tools to help protect businesses.

In this article, Ill delve into what AI is and how it applies to cybersecurity. Youll also learn the benefits and drawbacks of this promising technology. First, lets take a look at what AI is!

Artificial intelligence is a rationalization method using a statistically weighted matrix. This matrix is also called a neural net. You can think of this net as a decision matrix with nodes that have a weighted bias for each filtering process. The neural net will receive a database of precompiled data. This data will also contain answers to the underlying question the AI solves. This way, the AI will create a bias.

For example, lets consider a database containing different images. Lets say it has images of a persons face and other images of watermelons. Additionally, each image has a tag to check each item. As the AI learns whether it guessed correctly or not, the system increments node weightings. This process continues until the system reaches a predefined error percentage. This is often referred to as deep learning, which refers to the decision layers creating the depth.

Now, lets take a look at the steps used to process data.

We can condense the overall data workflow into the following process:

However, this process is slightly different with deep learning. The first step would include data from a precompiled database tagged with the correct response. Additionally, deep learning will repeat steps 1 through 4 to reach a predefined error tolerance value.

Lets take a look at this with an example of how AI data is processed.

Lets say a picture has reached an AI node. The node will filter the data into a usable format like 255 grayscale. Then, itll run a script to identify features, for example. If these features match others from a filter, the node can make a decision. For instance, itll say whether it found a face or a watermelon.

Then, the data goes to the next node down. This specific node could have a color filter to confirm the first decision. The process continues until the data reaches the last node. At that point, the AI will have made a final decision, ensuring whether it found a face or a watermelon.

Importantly, AI systems will always have a degree of error to them. None are infallible, and they never will be. But sometimes, the error percentages could be acceptable.

Now that you know how AI works lets take a look at AI cybersecurity solutions.

AI cybersecurity addresses the need to automate the assessment of threats in complex environments. Specifically, here are 2 use-cases for AI in AI cybersecurity:

Now you know the two main uses of AI in cybersecurity, lets take a look at its benefits and drawbacks!

As mentioned, AI has a lot of benefits. It runs repetitive tasks to identify anomalies or to classify data in particular in your business. That said, a few large drawbacks may offset its benefits. Here, well look at the drawbacks.

The first drawback is the AI cybersecurity solutions accuracy. This accuracy also depends on many factors. This includes the neural nets size and the decisions defined for filtering. It also depends on the number of iterations used to reach the predefined error percentage.

Imagine you have a decision tree with three layers. And each layer has several nodes for each decision route. Even though this is a fairly simple matrix, it needs a lot of calculations. Your systems finite resources will compromise your solutions intelligence.

An AI cybersecurity solution provider may stunt its solutions intelligence/accuracy to meet the target demographic. But sometimes, the problem isnt intelligence. Instead, its low latency and security vulnerabilities. When searching for an AI cybersecurity solution, consider how secure it is in your network.

Once trained, an AI statistical weighted matrix is often not re-trained in service. Youll find this is due to the lack of processing resources available in hardware. Sometimes, the system learns something that makes it worse, reducing effectiveness. Conversely, humans learn iteratively. This means they cause a lot of accidents. As a result, solution providers must ensure the software meets specification requirements during use.

Cybersecurity often requires updates to counter new exploits. To this end, it takes a lot of power to train your AI. Additionally, your AI cybersecurity vendor will need to update regularly to address cyber threats.

That said, the AI component of an AI cybersecurity solution is for classifying data and assessing anomalies in baseline data. As a result, it doesnt cause an issue for malware list updates. This means you can still use AI cybersecurity.

Now you know the benefits and drawbacks of AI cybersecurity, lets take a look at some uses for this technology!

As mentioned, highly automated businesses have the weakest cybersecurity. Generally, automated environments will overlap information technology (IT), operational technology (OT), and the Internet of things (IoT). This is to improve productivity, reduce the unit cost of a product, and undercut the competition.

But this also creates vulnerabilities. To this end, AI cybersecurity is great for finding potential exploits in these companies. Solutions either inform the administrator or automatically apply patches.

However, this may not be enough. Cybercriminals are currently attacking large, highly integrated companies. To do that, they exploit OT, which has no security. This OT was meant for wired networks to send commands to hardware like plant equipment. This means it never posed a security weakness. But today, attackers use OT to access the rest of a network or take plant equipment offline.

OT risk management tools are becoming popular for the reasons mentioned above. These systems effectively take a real-time clone of the production environment. Then, they run countless simulations to find exploits.

The AI part of the system generally finds exploits. In that case, an administrator provides a solution. OT risk management software continually runs as manufacturing plant arrangements change to meet orders, projects, or supply demands.

In this scenario, AI systems use known malware from antivirus lists to try and find an entry route into the system. The task requires automated repetitive functions of a complex system. And this makes it perfect for AI

So when should you implement AI cybersecurity? Lets find out.

As discussed above, businesses that use manufacturing and plant equipment should use AI cybersecurity. In most cases, youll also need to look for an OT risk management solution to reduce risks associated with OT.

You also can use AI cybersecurity if your business uses IoT and IT. This way, you can reduce the risk to the network from exploits. IoT devices generally undercut competitors, so you bypass the cost of adding adequate security measures.

Finally, you can use AI even if your company only uses IT. AI helps assess irregular traffic, so it protects your gateways. Additionally, you can leverage AIs data analytics. This way, youll know if someone is using your hardware for malicious purposes.

Now you know all you need to get started with AI cybersecurity, lets wrap things up!

Youll likely use AI wherever you need automated repetitive tasks. AI also helps make decisions on complex tasks. This is why many cybersecurity solution providers use AI. In fact, these providers tools help meet the challenge of highly complex systems that have very poor security.

You can always benefit from AI cybersecurity. It doesnt matter how integrated your business technology is. AI functionality is also great for classifying data using intelligent operations. This way, you can speed up your search for malware. AI cybersecurity is also beneficial for finding abnormal use of the network.

Do you have more questions about AI cybersecurity? Check out the FAQ and Resources sections below!

An AI neural net is a statical weighted matrix. This matrix helps process input data based on decisions made at nodes with a calibrated bias. To optimize this bias, data gets iteratively passed through the matrix. After that, the success rate is assessed, and each weighting value brings incremental changes. This process is called deep learning.

AI intelligence refers to the AIs error tolerance and decision layers. In theory, you could have as many layers as needed to make an intelligent AI. However, training it with data to reach a high error tolerance could be processor-intensive. This training may also take too long to produce. As a result, the solution becomes ineffective.

AI is trained using data to meet a predefined error tolerance level. For instance, a self-driving car lasts 1,000,000 miles by design. In this case, the cars service life determines the AI error tolerance. The AI accuracy must likely be 99.99% correct during decision-making to meet the service life

Operations technology (OT) risk assessment software assesses the security risks of plant equipment. Plants, integrated oil supply chains, and manufacturing 3.0 or above are also prime targets for attacks. AI cybersecurity can help assess threats using a clone of the production system. This helps check routes from OT systems to the rest of the system.

Yes, AI cybersecurity works in real-time. This helps detect weaknesses in your network or cyber threats. For example, you can find weaknesses by assessing traffic data through gateways and other hardware. You also can use AI as a centralized OT risk assessment software. This will let you assess the network structure for threats.

Learn about the different types of malware your AI cybersecurity solution will have to deal with.

Find out more about AI cybersecurity.

Discover more about AI and deep learning.

Understand how you can protect your organization by following GRC.

Learn how you can make your OPSEC better.

The rest is here:

Artificial Intelligence in Cyber Security: Benefits and Drawbacks. - TechGenix

DUBLIN--(BUSINESS WIRE)--The "Artificial Intelligence Computing Software: Market Analysis" report has been added to ResearchAndMarkets.com's offering.

Market is predicted to grow from $ 6.9B in 2021 to $ 37.6B in 2026 and may become a new sector of the economy.

This research contains complete information of the AI-related processors specifications and capabilities which were produced by the key market players and start-ups.

This comprehensive analysis can aid you in your technology acquisitions or investment decisions related to the fast-growing AI processors market.

After the main breakthrough at the turn of the century AI started to incorporate more and more artificial neural networks, connected in an ever-growing number of layers, now known as Deep Learning (DL). They can compete and outperform classical ML techniques like clustering but are more flexible and can work with much more complex datasets, including images and audio.

As machine learning entered exponential growth, it expanded into areas usually dominated by high-performance computing - such as protein folding and many-particle interactions. At the same time, our lives become increasingly dependent on its availability and reliability. This poses a number of new technical challenges but at the same time opens a road to novel solutions and technologies, in a similar way as space exploration or fundamental physics does.

More so, the commercial success of AI-enabled systems (autopilots, image processing, speech recognition and translation, to name just a few) ensures that no shortage of funds could hinder this growth. It has clearly become a new industry, if not a sector of the economy, one that is gaining importance with every passing year.

As any industry, it depends on several factors to prosper. Rising consumer demand has led to the consensus of major forecasters on the rapid growth of the sector - around 40% yearly in the near future, so funds shortage is not an issue. Instead, we must concentrate on other requirements for the efficient functioning of the industry.

The three main components are the availability of processing tools, the abundance of raw materials, and the workforce. Raw materials in this case are represented by big data, and there is often more of it than our current systems can make sense of. The workforce also seems to grow sufficiently fast, as ML cements its place in the university curriculum. So the processing tools, as well as the available energy to run them are clear bottlenecks in the exponential growth.

The end of Moore's extrapolation law due to quantum tunnelling and such, which become increasingly important with the reduction in transistor size, sets clear bounds on where we can go. To ensure long-term investments in the industry, a clear strategy must be developed to offset what will happen in 10 years

Key Highlights

Key Topics Covered:

1. Deep learning challenges

1.1 Architectural limitations

1.2 Brief introduction to deep learning

1.3 Cutting corners

1.4 Processing tools

2. Market analysis

2.1 Market overview

2.2 CPU

2.3 Edge and Mobile

2.4 GPU

2.5 FPGA

2.6 ASIC

2.6.1 Tech giants

2.6.2 Startups

2.7 Neuromorphic processors

2.8 Photonic computing

3. Glossary

4. Infographics

For more information about this report visit https://www.researchandmarkets.com/r/5wsx87

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Artificial Intelligence Computing Software Market Analysis Report 2022: Complete Information of the AI-related Processors Specifications and...