SparkCognition Advances the Science of Artificial Intelligence with 85 Patents

With award-winning machine learning technology, a multinational footprint, and expert teams, SparkCognition builds artificial intelligence systems to …

AUSTIN, Texas, Oct. 12, 2020 /PRNewswire/ — SparkCognition, the world’s leading industrial artificial intelligence (AI) company, is pleased to announce significant progress in its efforts to develop state of the art AI algorithms and systems, through the award of a substantial number of new patents. Since January 1, 2020, SparkCognition has filed 29 new patents, expanding the company’s intellectual property portfolio to 27 awarded patents and 58 pending applications.

“Since SparkCognition’s inception, we have placed a major emphasis on advancing the science of AI through research – making advancement through innovation a core company value,” said Amir Husain, founder and CEO of SparkCognition, and a prolific inventor with over 30 patents. “At SparkCognition, we’ve built one of the leading Industrial AI research teams in the world. The discoveries made and the new paths blazed by our incredibly talented researchers and scientists will be essential to the future.”

SparkCognition’s patents have come from inventors in different teams across the organization, and display commercial significance and scientific achievements in autonomy, automated model building, anomaly detection, natural language processing, industrial applications, and foundations of artificial intelligence. A select few include surrogate-assisted neuroevolution, unsupervised model building for clustering and anomaly detection, unmanned systems hubs for dispatch of unmanned vehicles, and feature importance estimation for unsupervised learning. These accomplishments have been incorporated into SparkCognition’s products and solutions, and many have been published in peer-reviewed academic venues in order to contribute to the scientific community’s shared body of knowledge.

In June 2019, AI research stalwart and two-time Chair of the University of Texas Computer Science Department, Professor Bruce Porter, joined SparkCognition full time as Chief Science Officer, at which time he launched the company’s internal AI research organization. This team includes internal researchers, additional talent from a rotation of SparkCognition employees, and faculty from Southwestern University, the University of Texas at Austin, and the University of Colorado at Colorado Springs. The organization works to produce scientific accomplishments such as: the patents and publications listed above, advancing the science of AI, and supporting SparkCognition’s position as an industry leader.

“Over the past two years, we’ve averaged an AI patent submission nearly every two weeks. This is no small feat for a young company,” said Prof. Bruce Porter. “The sheer number of intelligent, science-minded people at SparkCognition keeps the spirit of innovation alive throughout the research organization and the entire company. I’m excited about what this team will continue to achieve going forward, and eagerly awaiting the great discoveries we will make.”

To learn more about SparkCognition, visit

About SparkCognition

With award-winning machine learning technology, a multinational footprint, and expert teams, SparkCognition builds artificial intelligence systems to advance the most important interests of society. Our customers are trusted with protecting and advancing lives, infrastructure, and financial systems across the globe. They turn to SparkCognition to help them analyze complex data, empower decision-making, and transform human and industrial productivity. SparkCognition offers four main products: DarwinTM, DeepArmor®️, SparkPredict®️, and DeepNLPTM. With our leading-edge artificial intelligence platforms, our clients can adapt to a rapidly changing digital landscape and accelerate their business strategies. Learn more about SparkCognition’s AI applications and why we’ve been featured in CNBC’s 2017 Disruptor 50, and recognized four years in a row on CB Insights AI 100, by visiting

For Media Inquiries:

Michelle Saab


VP, Marketing Communications


Researchers Develop New Tool to Fight Bias in Computer Vision

Appen, a global leader in high-quality training data for machine learning systems, has partnered with the World Economic Forum to design and …

When discussing Artificial Intelligence (AI), a common debate is whether AI is an existential threat. The answer requires understanding the technology behind Machine Learning (ML), and recognizing that humans have the tendency to anthropomorphize. We will explore two different types of AI, Artificial Narrow Intelligence (ANI) which is available now and is cause for concern, and the threat which is most commonly associated with apocalyptic renditions of AI which is Artificial General Intelligence (AGI).

Artificial Narrow Intelligence Threats

To understand what ANI is you simply need to understand that every single AI application that is currently available is a form of ANI. These are fields of AI which have a narrow field of specialty, for example autonomous vehicles use AI which is designed with the sole purpose of moving a vehicle from point A to B. Another type of ANI might be a chess program which is optimized to play chess, and even if the chess program continuously improves itself by using reinforcement learning, the chess program will never be able to operate an autonomous vehicle.

With its focus on whatever operation it is responsible for, ANI systems are unable to use generalized learning in order to take over the world. That is the good news; the bad news is that with its reliance on a human operator the AI system is susceptible to biased data, human error, or even worse, a rogue human operator.

AI Surveillance

There may be no greater danger to humanity than humans using AI to invade privacy, and in some cases using AI surveillance to completely prevent people from moving freely. China, Russia, and other nations passed through regulations during COVID-19 to enable them to monitor and control the movement of their respective populations. These are laws which once in place, are difficult to remove, especially in societies that feature autocratic leaders.

In China, cameras are stationed outside of people’s homes, and in some cases inside the person’s home. Each time a member of the household leaves, an AI monitors the time of arrival and departure, and if necessary alerts the authorities. As if that was not sufficient, with the assistance of facial recognition technology, China is able to track the movement of each person every time they are identified by a camera. This offers absolute power to the entity controlling the AI, and absolutely zero recourse to its citizens.

Why this scenario is dangerous, is that corrupt governments can carefully monitor the movements of journalists, political opponents, or anyone who dares to question the authority of the government. It is easy to understand how journalists and citizens would be cautious to criticize governments when every movement is being monitored.

There are fortunately many cities that are fighting to prevent facial recognition from infiltrating their cities. Notably, Portland, Oregon has recently passed a law that blocks facial recognition from being used unnecessarily in the city. While these changes in regulation may have gone unnoticed by the general public, in the future these regulations could be the difference between cities that offer some type of autonomy and freedom, and cities that feel oppressive.

Autonomous Weapons and Drones

Over 4500 AI researches have been calling for a ban on autonomous weapons and have created the Ban Lethal Autonomous Weapons website. The group has many notable non-profits as signatories such as Human Rights Watch, Amnesty International, and the The Future of Life Institute which in itself has a stellar scientific advisory board including Elon Musk, Nick Bostrom, and Stuart Russell.

Before continuing I will share this quote from The Future of Life Institute which best explains why there is clear cause for concern: “In contrast to semi-autonomous weapons that require human oversight to ensure that each target is validated as ethically and legally legitimate, such fully autonomous weapons select and engage targets without human intervention, representing complete automation of lethal harm. ”

Currently, smart bombs are deployed with a target selected by a human, and the bomb then uses AI to plot a course and to land on its target. The problem is what happens when we decide to completely remove the human from the equation?

When an AI chooses what humans need targeting, as well as the type of collateral damage which is deemed acceptable we may have crossed a point of no return. This is why so many AI researchers are opposed to researching anything that is remotely related to autonomous weapons.

There are multiple problems with simply attempting to block autonomous weapons research. The first problem is even if advanced nations such as Canada, the USA, and most of Europe choose to agree to the ban, it doesn’t mean rogue nations such as China, North Korea, Iran, and Russia will play along. The second and bigger problem is that AI research and applications that are designed for use in one field, may be used in a completely unrelated field.

For example, computer vision continuously improves and is important for developing autonomous vehicles, precision medicine, and other important use cases. It is also fundamentally important for regular drones or drones which could be modified to become autonomous. One potential use case of advanced drone technology is developing drones that can monitor and fight forest fires. This would completely remove firefighters from harms way. In order to do this, you would need to build drones that are able to fly into harms way, to navigate in low or zero visibility, and are able to drop water with impeccable precision. It is not a far stretch to then use this identical technology in an autonomous drone that is designed to selectively target humans.

It is a dangerous predicament and at this point in time, no one fully understands the implications of advancing or attempting to block the development of autonomous weapons. It is nonetheless something that we need to keep our eyes on, enhancing whistle blower protection may enable those in the field to report abuses.

Rogue operator aside, what happens if AI bias creeps into AI technology that is designed to be an autonomous weapon?

AI Bias

One of the most unreported threats of AI is AI bias. This is simple to understand as most of it is unintentional. AI bias slips in when an AI reviews data that is fed to it by humans, using pattern recognition from the data that was fed to the AI, the AI incorrectly reaches conclusions which may have negative repercussions on society. For example, an AI that is fed literature from the past century on how to identify medical personnel may reach the unwanted sexist conclusion that women are always nurses, and men are always doctors.

A more dangerous scenario is when AI that is used to sentence convicted criminals is biased towards giving longer prison sentences to minorities. The AI’s criminal risk assessment algorithms are simply studying patterns in the data that has been fed into the system. This data indicates that historically certain minorities are more likely to re-offend, even when this is due to poor datasets which may be influenced by police racial profiling. The biased AI then reinforces negative human policies. This is why AI should be a guideline, never judge and jury.

Returning to autonomous weapons, if we have an AI which is biased against certain ethnic groups, it could choose to target certain individuals based on biased data, and it could go so far as ensuring that any type of collateral damage impacts certain demographics less than others. For example, when targeting a terrorist, before attacking it could wait until the terrorist is surrounded by those who follow the Muslim faith instead of Christians.

Fortunately, it has been proven that AI that is designed with diverse teams are less prone to bias. This is reason enough for enterprises to attempt when at all possible to hire a diverse well-rounded team.

Artificial General Intelligence Threats

It should be stated that while AI is advancing at an exponential pace, we have still not achieved AGI. When we will reach AGI is up for debate, and everyone has a different answer as to a timeline. I personally subscribe to the views of Ray Kurzweil, inventor, futurist, and author of ‘The Singularity is Near” who believes that we will have achieved AGI by 2029.

AGI will be the most transformational technology in the world. Within weeks of AI achieving human-level intelligence, it will then reach superintelligence which is defined as intelligence that far surpasses that of a human.

With this level of intelligence an AGI could quickly absorb all human knowledge and use pattern recognition to identify biomarkers that cause health issues, and then treat those conditions by using data science. It could create nanobots that enter the bloodstream to target cancer cells or other attack vectors. The list of accomplishments an AGI is capable of is infinite. We’ve previously explored some of the benefits of AGI.

The problem is that humans may no longer be able to control the AI. Elon Musk describes it this way: ”With artificial intelligence we are summoning the demon.’ Will we be able to control this demon is the question?

Achieving AGI may simply be impossible until an AI leaves a simulation setting to truly interact in our open-ended world. Self-awareness cannot be designed, instead it is believed that an emergent consciousness is likely to evolve when an AI has a robotic body featuring multiple input streams. These inputs may include tactile stimulation, voice recognition with enhanced natural language understanding, and augmented computer vision.

The advanced AI may be programmed with altruistic motives and want to save the planet. Unfortunately, the AI may use data science, or even a decision tree to arrive at unwanted faulty logic, such as assessing that it is necessary to sterilize humans, or eliminate some of the human population in order to control human overpopulation.

Careful thought and deliberation needs to be explored when building an AI with intelligence that will far surpasses that of a human. There have been many nightmare scenarios which have been explored.

Professor Nick Bostrom in the Paperclip Maximizer argument has argued that a misconfigured AGI if instructed to produce paperclips would simply consume all of earths resources to produce these paperclips. While this seems a little far fetched, a more pragmatic viewpoint is that an AGI could be controlled by a rogue state or a corporation with poor ethics. This entity could train the AGI to maximize profits, and in this case with poor programming and zero remorse it could choose to bankrupt competitors, destroy supply chains, hack the stock market, liquidate bank accounts, or attack political opponents.

This is when we need to remember that humans tend to anthropomorphize. We cannot give the AI human-type emotions, wants, or desires. While there are diabolical humans who kill for pleasure, there is no reason to believe that an AI would be susceptible to this type of behavior. It is inconceivable for humans to even consider how an AI would view the world.

Instead what we need to do is teach AI to always be deferential to a human. The AI should always have a human confirm any changes in settings, and there should always be a fail-safe mechanism. Then again, it has been argued that AI will simply replicate itself in the cloud, and by the time we realize it is self-aware it may be too late.

This is why it is so important to open source as much AI as possible and to have rational discussions regarding these issues.


There are many challenges to AI, fortunately, we still have many years to collectively figure out the future path that we want AGI to take. We should in the short-term focus on creating a diverse AI workforce, that includes as many women as men, and as many ethnic groups with diverse points of view as possible.

We should also create whistleblower protections for researchers that are working on AI, and we should pass laws and regulations which prevent widespread abuse of state or company-wide surveillance. Humans have a once in a lifetime opportunity to improve the human condition with the assistance of AI, we just need to ensure that we carefully create a societal framework that best enables the positives, while mitigating the negatives which include existential threats.

Charlotte Brunquet, Quilt.AI senior data scientist, speaks on prioritizing ethics and humanity in …

I took online courses on data science and machine learning and learned to code in Python. I was also looking for a sense of purpose in my job, …

Charlotte Brunquet is a senior data scientist at Quilt.AI. She has nine years of experience in data analytics and tech, supply chain management, and project management across Europe and Asia, including six years in the FMCG industry with Procter & Gamble.

The following interview was conducted by Amel Rigneau, founder of DigitalMind Media. It has been edited for brevity and clarity.

Amel Rigneau (AR): How did you become a data scientist?

Charlotte Brunquet (CB): After almost six years in supply chain roles with P&G France, I joined Bolloré Logistics to create the Digital innovation department in Singapore. I launched global programs, such as RPA (robotic process automation) to automate manual and repetitive tasks. That’s when I realized that I enjoyed coding and using my technical skills to build business solutions. I took online courses on data science and machine learning and learned to code in Python. I was also looking for a sense of purpose in my job, so when I met the CEO of Quilt.AI, a Singaporean startup working for both commercial companies and NGOs, I decided to leave the supply chain world and joined the startup as a data scientist.

Charlotte Brunquet is a senior data scientist at Quilt.AI. Courtesy of Charlotte Brunquet.

AR: How do you help companies improve their utilization of data?

CB: I work with anthropologists and sociologists to understand human culture and behavior. They mostly rely on quantitative surveys and focus groups to gain insights, such as consumer preferences and growth strategies.

Working hand in hand with researchers, our team of engineers created machine learning models that recognize emotions from pictures, identify user profiles based on their bio, or capture highlight moments in videos. When running those algorithms on large data sets pulled from all social media platforms, we can build a comprehensive and empathetic understanding of people.

What excites me the most is to apply these technologies to the nonprofit sector. NGOs want to better understand the needs of the communities they are helping, but also how to support them more efficiently and drive behavior changes. One of the things we’ve done was to help foundations understand the anti-vaccine movement in India, by analyzing Twitter profiles of users spreading such messages and their followers, as well as Google searches and YouTube videos.

AR: We hear a lot about the negative impacts of AI, such as job losses, discrimination, or manipulation. How do you ensure that your technology is used for good?

CB: Technology itself is neutral, it’s never good or bad. It all depends on how it’s being used. Nuclear fission enabled carbon-free energy, but also led to the creation of a weapon of mass destruction. The internet gave access to information to almost everyone around the world, but also led to the creation of the dark web. The same goes for AI. The risk of negative applications doesn’t mean that we have to stop using new technology and deprive ourselves of its benefits.

At Quilt.AI, we follow a couple of ground rules to ensure we prioritize ethics and humanity.

  • We never do “behavior change” projects for commercial companies, only for nonprofit organizations.
  • We don’t share any individual data and we use publicly available data only.
  • Finally, we don’t do any studies on people under the age of 18.

Of course, other organizations around the world could be doing the same but without these priorities in mind. That’s why it’s urgent for governments and institutions to establish rules that regulate AI on a global scale. In April 2019, the EU released a set of ethical guidelines, promoting development, deployment, and use of trustworthy AI technology. The OECD has published principles which emphasize the development of AI that respects human rights and democratic values, ensuring those affected by an AI system “understand the outcome and can challenge it if they disagree.” However, none of those documents provide regulatory mandates, they are just guidelines to shape conversations about the use of AI.

AR: What are the limitations of AI? What role do humans play in this increasingly automated world?

CB: We should not forget is that AI is trained by humans. We are still far away from general artificial intelligence, when machines can replicate the full range of human abilities, and there is no consensus on if and when this will be achieved. What AI lacks is the ability to apply the knowledge gained from one domain to another, as well as common sense.

A study by Cornell University gives us an example: the researchers tricked a neural network algorithm trained to recognize objects in images by introducing an elephant in a living room scene. Previously, the algorithm was able to recognize all objects—chair, coach, television, person, book, handbag, and so on—with high accuracy. However, as soon as the elephant was introduced, it became really confused and misidentified objects that were correctly detected before, even if they were located far away from the elephant in the image. In the field of market research specifically, AI still does not perform well when it comes to understanding context, sarcasm, or irony.

Algorithms help us to identify trends as well as prevailing emotions and topics, but we still need human interpretation to process the full complexity of understanding another human being.

AR: What is something you’ve enjoyed working on in the field of AI?

CB: In my current team, our objective is to build “empathy at scale” by creating algorithms that understand all facets of human personalities and culture without bias. A prerequisite for success is to be inclusive from the start, and that’s where our team of anthropologists comes into play. We recently built an application that identifies people’s cultural style—hipster, corporate, punk, hippie, and so on—instead of the usual gender and ethnicity recognition tools, and people love it.

Disclaimer: This article was written by a community contributor. All content is written by and reflects the personal perspective of the interviewee herself. If you’d like to contribute, you can apply here.

French Tech Singapore is a nonprofit organization gathering French entrepreneurs and locals working in Singapore in the tech industry. La French Tech encompasses all startups, i.e. all growth companies that share a global ambition, at every stage in their development, from embryonic firms to growing startups with several hundred employees and their sights set on the international market. As is the case all over the world, digital technology is a major catalyst for its development, and French Tech represents digital pure players as well as startups in medtech, biotech, cleantech, and other fields.

Artificial Intelligence (AI) in Medical Market Rising Trends and Technology Advancements 2020-2026

Final Report will add the analysis of the impact of COVID-19 on this industry. MarketIntelligenceData report, titled Global Artificial Intelligence (AI) in …

Final Report will add the analysis of the impact of COVID-19 on this industry.

MarketIntelligenceData report, titled Global Artificial Intelligence (AI) in Medical Market Size and Forecast to 2025 presents a comprehensive take on the overall market. Analysts have carefully evaluated the milestones achieved by the global Artificial Intelligence (AI) in Medical market and the current trends that are likely to shape its future. Primary and secondary research methodologies have been used to put together an exhaustive report on the subject. Analysts have offered unbiased outlook on the global Artificial Intelligence (AI) in Medical market to guide clients toward a well-informed business decision.

The Global Artificial Intelligence (AI) in Medical Market offers useful insights into the trends and the factors that propel this Global market. This market study comprehensively discusses the salient features of the Global Artificial Intelligence (AI) in Medical Market in terms of the market structure and landscape, the challenges, demand factors, and the expected market performance.

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Top companies operating in the Global Artificial Intelligence (AI) in Medical market profiled in the report are:

NVIDIA, Intel, Google, Microsoft, IBM, Siemens Healthineers, AWS, Medtronic, GE Healthcare

Global Artificial Intelligence (AI) in Medical Market Split by Product Type and Applications:

Market Segment by Type, covers:




Market Segment by Applications, covers:

Auxiliary Diagnosis

Drug Discovery

Health Management

Hospital Management


Regional Analysis For Artificial Intelligence (AI) in Medical Market:

North America (United States, Canada and Mexico)

Europe (Germany, France, UK, Russia and Italy)

Asia-Pacific (China, Japan, Korea, India and Southeast Asia)

South America (Brazil, Argentina, Colombia etc.)

Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria and South Africa)

Influence of the Artificial Intelligence (AI) in Medical Market Report:

-Comprehensive assessment of all opportunities and risk in the Artificial Intelligence (AI) in Medical market.

-Artificial Intelligence (AI) in Medical market recent innovations and major events.

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-Conclusive study about the growth plot of Artificial Intelligence (AI) in Medical market for forthcoming years.

-In-depth understanding of Artificial Intelligence (AI) in Medical market-particular drivers, constraints and major micro markets.

-Favourable impression inside vital technological and market latest trends striking the Artificial Intelligence (AI) in Medical market.

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What are the market factors that are explained in the report?

Executive Summary:It includes key trends of the global Artificial Intelligence (AI) in Medical market related to products, applications, and other crucial factors. It also provides analysis of the competitive landscape and CAGR and market size of the global Artificial Intelligence (AI) in Medical market based on production and revenue.

Production and Consumption by Region:It covers all regional markets focused in the research study. It discusses about prices and key players besides production and consumption in each regional market.

Key Players: Here, the report throws light on financial ratios, pricing structure, production cost, gross profit, sales volume, revenue, and gross margin of leading and prominent companies competing in the global Artificial Intelligence (AI) in Medical market.

Market Segments:This part of the report discusses about product type and application segments of the global Artificial Intelligence (AI) in Medical market based on market share, CAGR, market size, and various other factors.

Research Methodology:This section discusses about the research methodology and approach Artificial Intelligence (AI) in Medical used to prepare the report. It covers data triangulation, market breakdown, market size estimation, and research design and/or programs.

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Artificial Intelligence (AI) in Cyber Security Market Analysis, Size, Share, Growth, Trends And …

Machine learning is taking the most market percentage, with over 69% market share. Artificial Intelligence (AI) in Cyber Securit. By Geographical …

IndustryGrowthInsights (IGI), one of the world’s prominent market research firms has released a new report on Global Artificial Intelligence (AI) in Cyber Security Market. The report contains crucial insights on the market which will support the clients to make the right business decisions. This research will help both existing and new aspirants for Artificial Intelligence (AI) in Cyber Security market to figure out and study market needs, market size, and competition. The report talks about the supply and demand situation, the competitive scenario, and the challenges for market growth, market opportunities, and the threats faced by key players.

The report also includes the impact of ongoing global crisis i.e. COVID-19 on the Artificial Intelligence (AI) in Cyber Security market and what the future holds for it. The published report is designed using a vigorous and thorough research methodology and IndustryGrowthInsights (IGI) is also known for its data accuracy and granular market reports.

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A complete analysis of the competitive scenario of the Artificial Intelligence (AI) in Cyber Security market is depicted by the report. The report has a vast amount of data about the recent product and technological developments in the markets. It has a wide spectrum of analysis regarding the impact of these advancements on the market’s future growth, wide-range of analysis of these extensions on the market’s future growth.

Artificial Intelligence (AI) in Cyber Security market report tracks the data since 2015 and is one of the most detailed reports. It also contains data varying according to region and country. The insights in the report are easy to understand and include pictorial representations. These insights are also applicable in real-time scenarios.

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Components such as market drivers, restraints, challenges, and opportunities for Artificial Intelligence (AI) in Cyber Security are explained in detail. Since the research team is tracking the data for the market from 2015, therefore any additional data requirement can be easily fulfilled.

Some of the prominent companies that are covered in this report:

BAE Systems




Check Point


RSA Security


Juniper Network

Palo Alto Networks

Artificial Intelligence (AI) in Cyber Securit

*Note: Additional companies can be included on request

The industry looks to be fairly competitive. To analyze any market with simplicity the market is fragmented into segments, such as its product type, application, technology, end-use industry, etc. Segmenting the market into smaller components helps in understanding the dynamics of the market with more clarity. Data is represented with the help of tables and figures that consist of a graphical representation of the numbers in the form of histograms, bar graphs, pie charts, etc. Another key component that is included in the report is the regional analysis to assess the global presence of the Artificial Intelligence (AI) in Cyber Security market.

Following is the gist of segmentation:

By Application:



IT & Telecom


Aerospace and Defense


BFSI, government and IT & telecom segments occupied the largest market share, while healthcare, aerospace and defense and other industries are expected to grow at a steady speed in future.

By Type:

Machine Learning

Natural Language Processing


Machine learning is taking the most market percentage, with over 69% market share.

Artificial Intelligence (AI) in Cyber Securit

By Geographical Regions

Asia Pacific: China, Japan, India, and Rest of Asia Pacific

Europe: Germany, the UK, France, and Rest of Europe

North America: The US, Mexico, and Canada

Latin America: Brazil and Rest of Latin America

Middle East & Africa: GCC Countries and Rest of Middle East & Africa

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Executive Summary

Assumptions and Acronyms Used

Research Methodology

Artificial Intelligence (AI) in Cyber Security Market Overview

Global Artificial Intelligence (AI) in Cyber Security Market Analysis and Forecast by Type

Global Artificial Intelligence (AI) in Cyber Security Market Analysis and Forecast by Application

Global Artificial Intelligence (AI) in Cyber Security Market Analysis and Forecast by Sales Channel

Global Artificial Intelligence (AI) in Cyber Security Market Analysis and Forecast by Region

North America Artificial Intelligence (AI) in Cyber Security Market Analysis and Forecast

Latin America Artificial Intelligence (AI) in Cyber Security Market Analysis and Forecast

Europe Artificial Intelligence (AI) in Cyber Security Market Analysis and Forecast

Asia Pacific Artificial Intelligence (AI) in Cyber Security Market Analysis and Forecast

Asia Pacific Artificial Intelligence (AI) in Cyber Security Market Size and Volume Forecast by Application

Middle East & Africa Artificial Intelligence (AI) in Cyber Security Market Analysis and Forecast

Competition Landscape

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