Guest post from Jingchen Zhao, professor of law at Nottingham Law School

Zhao: AI brings many positive changes

Artificial intelligence (AI) is taking the business world by storm with its capability to collect, filter and react to data rapidly and in many different ways.

Applying AI technology to law firms involves the use of computers, algorithms and big data to assist, support, collaborate or even duplicate lawyers behaviours and decisions so that law firms can function competently, successfully and with foresight in their business environment.

The interconnected world enhanced by technologies such as AI brings many positive changes for the ways in which law firms communicate with their customers, clients and business partners, offering the advantage of providing more an efficient and effective service without compromising quality.

Although AI has not yet been developed to a level where AI-empowered legal advice could fully replace human legal practitioners, the adoption of AI has the potential to reduce transaction costs and improve the accessibility of legal advice through the use of automated assistants, digital hubs or software to offer AI-powered legal services for vulnerable clients.

In collaboration with the Hungarian digital law firm SimpLEGAL, InvestCEE LegalTech Consultancy issued AI in Legal Services A Practical Guide in December 2021, suggesting that AI offers new opportunities for digitalising legal services.

One of the most common ways of using AI in legal practice is to delegate certain tasks, especially where decisions need to be reached on the basis of a large quantity of data and legal practitioners are not capable of providing a swift response.

This kind of delegation can ease the tension between plausible hypotheses and the formal analysis of professional judgements by lawyers, allow the systematic study of issues in order to help legal practitioners make better decisions, and mitigate human limitations in terms of understanding complex data and making well-informed choices between the options available.

In addition to assistance with processing large quantities of data, efficient algorithms have empowered AI to make decisions at a near-instantaneous speed.

AI technologies are able to categorise solutions based on different criteria and priorities, assess the merits of each solution, and recommend a set of selected options for legal practitioners, who are then able to evaluate these solutions more efficiently and in a focused and informed manner.

This evaluation process can be made even more effective as algorithms can be configured to calculate and inform the confidence level of the selected options and assess the merits and disadvantages of each one.

In-house legal departments require more guidance in relation to the basic terminology used in the legal AI domain. When applying AI in a firm, it is also important to understand how this might change the firms risk profile, since AI can also be a disruptive technology, and accountable AI practice needs to be reinforced by regulatory insight to enable its sustainable development.

However, as yet no consensus has been reached on the most appropriate regulatory framework to achieve these goals.

The European Commission is taking a lead in terms of regulating AI globally, proposing a risk-based regulatory framework that involves determining the scale or scope of risks related to a concrete situation and a recognised threat.

This framework is also likely to be useful in unpacking the potential role and challenges of AI in promoting more accountable law firms and legal professionals, considering the benefits that accountable and sustainable AI could bring to law firms to protect their clients, particularly vulnerable ones.

By facilitating the use of AI services, the commissions regulatory framework should help law firms to identify and meet the needs of clients who may have difficulty using legal services, or who may be at risk of acting against their own best interests.

An appropriate regulatory framework to promote sustainable AI by monitoring and mitigating the associated risks in legal practice is a pre-condition for using AI more comprehensively in the legal domain.

Instead of being a free-standing regulatory intervention, I believe that an ideal approach will be to construct a regulatory agenda and a control strategy to be combined with other control strategies across different social, economic and cultural contexts and tasks.

The design of this framework should encourage the participation of stakeholders with different expertise such as computer scientists, representatives from industrial organisations, active shareholders, specialist committees and counsel, and consultants or partners with expert technological skill sets, as well as international agencies.

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Using artificial intelligence in the legal profession - Legal Futures

Throughout history, technology has never advanced as quickly as it is right now. It's becoming harder than ever for investors to track the sheer number of innovative tech companies in the public markets, each offering its own unique vision for the future.

But perhaps no technology is more transformative than artificial intelligence (AI), which is already being deployed to complete highly complex tasks in a fraction of the time that humans can. According to one estimate, up to 70% of companies worldwide will have integrated some form of AI into their businesses by 2030, adding $13 trillion in additional output to the global economy.

There will be no shortage of opportunities in the sector over the next decade, but these two stocks might be a great place to start given they're trading at hefty discounts to their all-time highs amid the broader tech sell-off.

C3ai (AI 5.18%) is a first-of-its-kind enterprise AI company. Its stock is volatile because the company isn't profitable yet, and its revenue growth has underperformed expectations since it was listed on the public markets in December 2020. But that's often part and parcel of breaking ground in a brand-new industry.

C3.ai is a good place to start for investors who want exposure to the artificial intelligence sector because it builds both ready-made and customizable AI applications for 11 different industries. For most of its customers, C3.ai is the one-stop source for their AI needs, and it's possible they wouldn't otherwise have access to the technology.

The oil and gas industry is C3.ai's largest contributor to revenue, making up 54% of its $252 million in fiscal 2022, which ended on April 30. The company's technology helps oil behemoths like Shelltrack thousands of pieces of equipment to predict potentially catastrophic failures, saving time, money, and negative environmental impacts. C3.ai has an entire suite of applications just for the fossil fuel sector, which also helps those companies manage carbon emissions to run cleaner businesses.

But C3.ai has also drawn recognition from the largest tech companies in the world. It has partnerships with both Microsoftand Alphabet'sGoogle to collaboratively develop AI applications to better serve their customers using cloud computing technology.

C3.ai's stock price is down 88% from its all-time high, so it carries inherent risks. The company lost $192 million in fiscal 2022 (which ended April 30), but importantly, it has $959 million in cash, equivalents, and short-term investments on its balance sheet -- which means it can run at that loss rate for the next five years before it needs more money. C3.ai has a high gross profit margin of 81%, so once it achieves scale, it can cut back its operating costs to generate positive earnings. But the key question is how long it will take to get there, if at all. With new businesses in new industries, it's always an unknown.

But C3.ai estimates its AI software opportunity could be worth $596 billion by 2025. Since the company's market value is only $2 billion now, it might be worth a small bet for investors with some risk appetite.

Upstart Holdings (UPST 9.54%) offers a great example of how artificial intelligence is being used to improve decades-old processes. Its AI-powered algorithm is designed to replace Fair Isaac's FICO credit scoring system, which is the traditional means of assessing a potential borrower's creditworthiness. Upstart can analyze as many as 1,600 data points about an applicant to deliver a loan decision almost instantly 74% of the time, a feat that might take human assessors days or even weeks to determine.

Fifty-seven banks and credit unions have signed on to use Upstart's algorithm, and one bank, in particular, has abandoned FICO scores altogether in its favor. This is key because Upstart isn't a lender; it originates loans for its bank partners in exchange for a fee. But the company was forced to deviate from this strategy slightly in the recent first quarter of 2022 amid turbulent credit market conditions. Upstart absorbed $345 million worth of new loans using its own balance sheet, which spooked investors.

This added to the $252 million worth of loans it already held mostly for research and development purposes. Management says the increase is only temporary, and it's important to note the $345 million jump represented just 7% of the total $4.5 billion in originations during the quarter.

It's partly a symptom of Upstart's rapid growth, which is bolstered by its entry into the automotive loan origination space. Since launching its car sales and origination software in 2021 called Upstart Auto Retail, 35 car makers have adopted the platform across 525 dealerships. That's up 224% from 162 dealers in Q1 last year.

Upstart generated $849 million in revenue during 2021, a whopping 264% year-over-year jump. It thinks revenue could top $1.25 billion this year, and while that's a slowdown in growth, consumers are contending with higher interest rates and tougher economic conditions, which could dampen demand for credit.

But the company continues to expand into what it estimates is a $6 trillion addressable opportunity. With its stock price down 90% from its all-time high, it might be a great chance to make a long-term bet on what could be the future of credit assessments.

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2 Artificial Intelligence Growth Stocks to Buy on the Dip - The Motley Fool

By using automation, how is artificial intelligence benefiting the Fintech industry in 2022?

Right present, the fintech business is undergoing a considerable transformation. Customers are benefiting from the disruption by having more accessible access to credit, which has made payments and transactions more accessible than ever before. All of this is feasible because of technological advancements such as open banking and the rise of AI and Machine Learning.

Young India is credit-hungry, and its per capita expenditure has been steadily increasing. Customers used to have to go to the bank location, physically produce the appropriate paperwork, and wait at least 15 days for credit or a loan until recently. Banks used to take a long time to process documents, conduct KYC through human visits, assess credit risk, and finally authorize loans. Banks and lenders, on the other hand, may now lend money in a matter of hours rather than days. This has made the entire loan cycle shorter and more accessible to the average person.

The Fintech sector has undergone a complete transformation because of digitalization, open APIs, and machine learning integration. Lenders may process loan applications, conduct e-KYCs, and credit assessments, assess creditworthiness, and process loan amounts in only a few minutes. This has opened up a lot of options for people looking for financing. Every month, millions of customers apply for a loan, but only 10 to 15% of them are successful in completing the application procedure, and only 2 to 5% of those who apply are approved.

Both pre-processing and post-processing steps are affected by loan dropout. Filling up the application, receiving an offer, presenting KYC papers, providing account statements, income tax returns, and so on are all examples of pre-processing phases. Credit evaluation, credit determination, and loan distribution are the steps of post-processing. Several causes contribute to loan dropout at various stages: the client does not complete the application or is unable to supply the required papers, does not meet the risk score requirements, is price sensitive, and so on.

The loss of consumers along the loan application journey has grown costly for digital lending organizations as customer acquisition expenses have risen, resulting in a significant loan drop at every stage. This is where AI-driven intelligent automation technologies are assisting financial institutions in not only automating the entire process but also drastically lowering their costs and even helping customers in making educated decisions during their loan application journey.

Furthermore, it is a time-consuming procedure for lending organizations to complete all of their research while relying on the expertise of credit risk managers, credit policymakers, legal resources, and an entire team to analyze customer paperwork and still fail. Given the large number of applicants in this digital era, its hard to explore all the papers, assess the risk, determine credit worthiness, and make the best judgments possible while minimizing risk.

To address this problematic issue, AI and machine learning-based intelligent automation systems have been created and implemented to handle massive amounts of data, categorize anomalies, evaluate payment behaviour and patterns, assess credit worthiness, and automate risk choices. AI is enabling credit risk managers to gain a scientific understanding of each customers identity and risk behaviour, as well as give credit risk causation. AI is assisting lenders in predicting client loan dropout probabilities, which may aid in screening out qualified candidates, allowing the funnel to be optimized and ultimately lead to quality consumers being targeted and the entire application process being improved.

Following the completion of loan applications, the overarching AI model aids in predicting which customers are most likely to have their loan approved and establishing a pattern for suitable applications. This enables lenders to identify high-quality clients ahead of time and focus their efforts entirely on helping them boost conversion rates and reduce loan default rates.

Client acquisition costs are also significantly reduced as a result of this. The lender might also employ AI-powered intelligent automation to predict which customers are most likely to abandon their digital loan application at critical phases like avail offer, KYC, and document submission. The potent mix of AI and automation produces a one-of-a-kind customer service approach that also helps to prevent loan default. Using this data, the lender may now optimize targeted consumer advertising and call centre activity.

Adopting digital technology such as artificial intelligence in loan application administration can help banks reorganize the customer journey, increase efficiency, and free up people to provide value-added services.

Edited by Prakriti Arora

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By Using Automation, How Is Artificial Intelligence Benefiting The Fintech Industry In 2022? - Inventiva

To many they are arts next big thing digital images of jellyfish pulsing and blurring in a dark pink sea, or dozens of butterflies fusing together into a single organism.

The Argentine artist Sofia Crespo, who created the works with the help of artificial intelligence, is part of the generative art movement, where humans create rules for computers which then use algorithms to generate new forms, ideas and patterns.

The field has begun to attract huge interest among art collectors and even bigger price tags at auction.

US artist and programmer Robbie Barrat a prodigy still only 22 years old sold a work called Nude Portrait#7Frame#64 at Sothebys in March for 630,000 ($821,000).

That came almost four years after French collective Obvious sold a work at Christies titled Edmond de Belamy largely based on Barrats code for $432,500.

Collector Jason Bailey told AFP that generative art was like a ballet between humans and machines.But the nascent scene could already be on the verge of a major shake-up, as tech companies begin to release AI tools that can whip up photo-realistic images in seconds.

Artists in Germany and the United States blazed a trail in computer-generated art during the 1960s.

The V&A museum in London keeps a collection going back more than half a century, one of the key works being a 1968 piece by German artist Georg Nees called Plastik 1.

Nees used a random number generator to create a geometric design for his sculpture.

Nowadays, digital artists work with supercomputers and systems known as Generative Adversarial Networks (GANs) to create images far more complex than anything Nees could have dreamed of.

GANs are sets of competing AIs - one generates an image from the instructions it is given, the other acts as a gatekeeper, judging whether the output is accurate.

If it finds fault, it sends the image back for tweaks and the first AI gets back to work for a second try to beat the gamekeeper.But artists like Crespo and Barrat insist that the artist is still central to the process, even if their working methods are not traditional.

When Im working this way, Im not creating an image. Im creating a system that can create images, Barrat told AFP.

Crespo said she thought her AI machine would be a true collaborator, but in reality it is incredibly tough to get even a single line of code to generate satisfactory results.

She said it was more like babysitting the machine. Tech companies are now hoping to bring a slice of this rarefied action to regular consumers.

Google and Open AI are both touting the merits of new tools they say bring photorealism and creativity without the need for coding skills.

They have replaced GANs with more user-friendly AI models called transformers that are adept at converting everyday speech into images.

Google Imagens webpage is filled with absurdist images generated by instructions such as: A small cactus wearing a straw hat and neon sunglasses in the Sahara desert.

Open AI boasts that its Dalle-2 tool can offer any scenario in any artistic style from the Flemish masters to Andy Warhol.

Although the arrival of AI has led to fears of humans being replaced by machines in fields from customer care to journalism, artists see the developments more as an opportunity than a threat.

Crespo has tried out Dalle-2 and said it was a new level in terms of image generation in general though she prefers her GANs. I very often dont need a model that is very accurate to generate my work, as I like very much when things look indeterminate and not easily recognizable, she said.

Camille Lenglois of Pariss Pompidou Centre Europes largest collection of contemporary art also played down any idea that artists were about to be replaced by machines.

She told AFP that machines did not yet have the critical and innovative capacity, adding: The ability to generate realistic images does not make one an artist.

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Is Artificial Intelligence the future of art? : - The Tico Times

Self-driving cars are nifty. Electric vehicles are cool. And when you think of self-driving electric cars, its hard not to think of Tesla. That said, not everyone associates them with safety. And with how the AIs algorithmic thinking is looking, they may have good reason.

On Thursday, the National Highway Traffic Safety Administration, an agency under the guidance of Transportation Secretary Pete Buttigieg, said it would be expanding a probe and look into830,000 Tesla carsacross all four current model lines, 11% more vehicles than they were previously examining.

Initially the probe started last year in response to Tesla vehicles mysteriously plowing into the scene of an existing accident where first responders were already present.

On Thursday, NHTSA said it had discovered in 16 separate instances when this occurred that Autopilot aborted vehicle control less than one second prior to the first impact, suggesting the driver was not prepared to assume full control over the vehicle.

CEO Elon Musk hasoften claimedthat accidents cannot be the fault of the company, as data it extracted invariably showed Autopilot was not active in the moment of the collision.

At least 26 crashes and 11 deaths appear to involve Teslas autopilot feature. While it is true that drivers should have their hands at 10 and 2 with their eyes on the road, youve gotta admit that there have been some representations of the autopilot feature as a replacement for human inputs. A last-minute shift from AI to UI is exactly the type of childish loopholing masquerading as brilliance youd expect from a guy with an Elden Ring build this bad.

Look, I know Ive made that gag in a prior article where I dunked on Musk for being goofy, BUT TWO MEDIUM SHIELDS?

For fear of being labeled a one-trick Tesla with weak windows this is exactly what youd expect from a guy who was already on trial for killing someone with a car.

Whats next? A special re-issue of O.J. Simpsons If I Did It with an additional chapter from Elon on how hed use tweets to manipulate stock prices?

Cartoonish evil gets satirical responses. In the meantime, it may be worth it to consider electric car alternatives that arent Teslas. And pay attention to the road, damn it.

Elon Musks Regulatory Woes Mount As U.S. Moves Closer To Recalling Teslas Self-Driving Software [Fortune]

Chris Williams became a social media manager and assistant editor for Above the Law in June 2021. Prior to joining the staff, he moonlighted as a minor Memelord in the Facebook groupLaw School Memes for Edgy T14s. He endured Missouri long enough to graduate from Washington University in St. Louis School of Law. He is a former boatbuilder who cannot swim,a published author on critical race theory, philosophy, and humor, and has a love for cycling that occasionally annoys his peers. You can reach him by email atcwilliams@abovethelaw.comand by tweet at@WritesForRent.

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Where Does Legal Accountability Rest Between Tesla's Artificial Intelligence and Human Error? - Above the Law

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Credit: CWRU

CLEVELANDCollaboration between pharmaceutical companies and the Center for Computational Imaging and Personalized Diagnostics (CCIPD) at Case Western Reserve University has led to the development of artificial intelligence (AI) tools to benefit patients with non-small cell lung cancer (NSCLC) based on an analysis of routine tissue biopsy images, according to new research.

This year, more than 236,000 adults in the United States will be diagnosed with lung cancerabout 82% of them with non-small cell lung cancer, according to the American Society of Clinical Oncology.

Researchers at the CCIPD used AI to identify biomarkers from biopsy images for patients with NSCLC, as well as gynecologic cancers, that help predict the response to immunotherapy and clinical outcomes, including survival.

We have shown that the spatial interplay of features relating to the cancer nuclei and tumor-infiltrating lymphocytes drives a signal that allows us to identify which patients are going to respond to immunotherapy and which ones will not, said Anant Madabhushi, CCIPD director and Donnell Institute Professor of Biomedical Engineering at Case Western Reserve.

The study was published this month in the journal Science Advances.

Immunotherapy is expensive, and studies show that only 20-30% of patients respond to the treatment, according to National Institutes of Health and other sources. These findings validate that the AI technologies developed by the CCIPD can help clinicians determine how best to treat patients with NSCLC and gynecologic cancers, including cervical, endometrial and ovarian cancer, Madabhushi said.

The study, drawn from a retrospective analysis of data, also revealed new biomarker information regarding a protein called PD-L1 that helps prevent immune cells from attacking non-harmful cells in the body.

Patients with high PD-L1 often receive immunotherapy as part of their treatment for NSCLC, while patients with low PD-L1 are often not offered immunotherapy, or its coupled with chemotherapy.

Our work has identified a subset of patients with low PD-L1 who respond very well to immunotherapy and may not require immunotherapy plus chemotherapy, Madabhushi said. This could potentially help these patients avoid the toxicity associated with chemotherapy while also having a favorable response to immunotherapy.

The multi-site, multi-institutional study examined three common immunotherapy drugs (called checkpoint inhibitor agents) that target PD-L1: atezolizumab, nivolumab and pembrolizumab. The AI tools consistently predicted the response and clinical outcomes for all three immunotherapies.

The study is part of broader research conducted at CCIPD to develop and apply novel AI and machine-learning approaches to diagnose and predict the therapy response for various diseases and cancers, including breast, prostate, head and neck, brain, colorectal, gynecologic and skin.

The study coincides with Case Western Reserve recently signing a license agreement with Picture Health to commercialize AI tools to benefit patients with NSCLC and other cancers.

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Case Western Reserve University is one of the country's leading private research institutions. Located in Cleveland, we offer a unique combination of forward-thinking educational opportunities in an inspiring cultural setting. Our leading-edge faculty engage in teaching and research in a collaborative, hands-on environment. Our nationally recognized programs include arts and sciences, dental medicine, engineering, law, management, medicine, nursing and social work. About 5,800 undergraduate and 6,300 graduate students comprise our student body. Visitcase.eduto see how Case Western Reserve thinks beyond the possible.

Spatial interplay patterns of cancer nuclei and tumor-infiltrating lymphocytes (TILs) predict clinical benefit for immune checkpoint inhibitors

1-Jun-2022

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

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Artificial intelligence tool predicts response to immunotherapy in lung and gynecologic cancer patients - EurekAlert

This accredited course is designed to give you industry experience alongside our research-led teaching.

We encourage you to take summer work placements in an industry of your choice or even add a full year in industry to help you gain the experience you need for accreditation.

All our computer science degree courses share the same compulsory modules in years 1 and 2, making it easy to switch between them. In the third and fourth years, you can tailor your degree by choosing optional modules.

Youll study the logical and mathematical theory underpinning computer science. Youll also gain an understanding of the fundamentals of computer hardware.

As an introduction to software engineering, youll cover data structures and algorithms. Youll also look at the principles of AI programming, including using an object-oriented approach and software engineering processes.

Youll apply your knowledge by working on practical projects. For example, youll build algorithms and data analysis tools, and develop software user interfaces.

Youll deepen your understanding of computer science by studying topics, such as artificial intelligence, communication protocols and the TCP/IP layered model.

A group project will give you first-hand experience of working in a team, and of the problems of communication and scale in software engineering.

An individual project is a chance to explore in depth an area of AI that interests you, under the supervision of an academic who is doing work in that area. Recent topics include:

Youll take a compulsory module in engineering management and law. Youll also specialise in artificial intelligence choosing options such as machine learning, simulation and advanced robotics.

You could also study a language, take modules from other disciplines such as psychology or chemistry, or choose from a range of innovative interdisciplinary modules.

Youll take part in a group design project. This involves working in a team for an industry or academic customer to solve a real-world problem. For example, previous students built an AI system for Ordnance Survey for a project entitled learning from aerial imagery.

Optional modules cover topics such as machine learning, computational finance and biologically inspired robots.

There is also an opportunity to study abroad for a semester.

Want more detail?See all the modules in the course.

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Computer Science & Artificial Intelligence - University of Southampton

The year 1 and 2 modules are similar across all our Electronic Engineering courses and provide a grounding in essential engineering topics.

In years 3 and 4 youll specialise in AI, and can follow your interests by choosing modules from a wide range of options. You can also take modules from other subject areas.

Youll work in high-spec electronics and computer labs, equipped with the latest technology, hardware and software.

In the first year, youll study digital systems, and electrical materials and fields. There are core modules in:

mathematics

physics

electronics

programming

We'll develop your practical skills with extensive laboratory classes. In your first semester youll get to build processing boards.

Compulsory modules will explore:

electrical materials

circuitry

programming

electronic design

You'll choose from optional modules, covering topics such as:

photonics

semiconductors

computer engineering

At the end of the year, you'll complete a 3-week team challenge, judged by an industry panel. Previous projects include the development of a home AI system and building a quadcopter.

Youll complete a unique piece of individual research in an AI topic of your choice. This will typically involve designing, building and testing a new electronic system. Past students have designed a traffic counting system using computer vision, and explored security for smart home systems.

You'll study the foundations of machine learning, and select specialised optional modules such as:

robotic systems

computational biology

cyber security

green electronics

You can also choose to:

The main group design project is a great opportunity to experience working for an industry or academic customer. Past projects have involved:

Youll also select from optional modules covering topics, such as:

machine Learning

data mining

computer vision

You can apply to spend the second semester studying abroad at a partner institution.

Want more detail?See all the modules in the course.

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Artificial Intelligence Engineering | University of Southampton

Redding, California, June 09, 2022 (GLOBE NEWSWIRE) -- According to a new market research report titled, AI in Cybersecurity Market by Technology (ML, NLP), Security (Endpoint, Cloud, Network), Application (DLP, UTM, IAM, Antivirus, IDP), Industry (Retail, Government, BFSI, IT, Healthcare), and Geography - Global Forecasts to 2029, the global artificial intelligence in cybersecurity market is expected to grow at a CAGR of 24.2% during the forecast period to reach $66.22 billion by 2029.

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The increasing demand for advanced cybersecurity solutions and privacy, the growing significance of AI-based cybersecurity solutions in the banking sector, the rising frequency and complexity of cyber threats are the key factors driving the growth of the artificial intelligence in cybersecurity market. In addition, the growing need for AI-based cybersecurity solutions among small and medium-sized enterprises (SMEs) are creating new growth opportunities for vendors in the AI in cybersecurity market.

However, the lack of skilled AI professionals, the perception of AI in cybersecurity as an uncomprehensive security solution, and the impacts of the COVID-19 pandemic are expected to restrain the growth of this market to a notable extent.

The global artificial intelligence in cybersecurity market is segmented based on components (hardware, software, services), technology (machine learning, natural language processing, context-aware computing), security (application security, endpoint security, cloud security, network security), by applications (data loss prevention, unified threat management, encryption, identity & access management, risk & compliance management, antivirus/antimalware, intrusion detection/prevention system, distributed denial of service mitigation, security information & event management, threat intelligence, fraud detection), by deployment (on-premises, cloud-based), industry vertical (retail, government & defense, automotive & transportation, BFSI, manufacturing, infrastructure, IT & telecommunication, healthcare, aerospace, education, energy). The study also evaluates industry competitors and analyses the market at the country level.

Based on component, the AI in cybersecurity market is segmented into software, hardware, and services. In 2022, the software segment is estimated to account for the largest share of the artificial intelligence in cybersecurity market. The larger share and highest CAGR of this segment is primarily driven by the growing data security concerns, the increase in demand for AI platforms solutions for security operations, the surge in demand for robust and cost-effective security solutions among business enterprises to strengthen their cybersecurity infrastructure.

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Based on technology, the market is segmented into machine learning, natural language processing (NLP), and context-aware computing. In 2022, the machine learning technology segment is estimated to account for the largest share of the artificial intelligence in cybersecurity market. The large share and highest CAGR of this segment is primarily attributed to its advanced ability to collect, process, and handle big data from different sources that offer rapid analysis and prediction. It also helps analyze user behavior and learn from them to help prevent attacks and respond to changing behavior. In addition, it helps find threats and respond to active attacks in real-time, reduces the amount of time spent on routine tasks, and enables organizations to use their resources more strategically, further supporting the growth of the machine learning technology market in the coming years.

Based on security, the market is segmented into network security, cloud security, endpoint security, and application security. In 2022, the network security segment is estimated to account for the largest share of the artificial intelligence in cybersecurity market. The large share of this segment is attributed to the adoption of the Bring Your Own Device (BYOD) trend, the increasing number of APTs, malware, and phishing attacks, the increasing need for secure data transmission, the growing demand for network security solutions, and rising privacy concerns. However, the cloud security segment is slated to register the highest CAGR during the forecast period due to the increased adoption of Internet of Things (IoT) devices, surge in the deployment of cloud solutions, the emergence of remote work and collaboration, the increasing demand for robust and cost-effective security services.

Based on application, this market is segmented into data loss prevention, unified threat management, encryption, identity & access management, risk & compliance management, intrusion detection/prevention system, antivirus/antimalware, distributed denial of service (DDoS) mitigation, Security Information and event management (SIEM), threat intelligence, and fraud detection. In 2022, the identity and access management segment is estimated to account for the largest share of the artificial intelligence in cybersecurity market. The large share of this segment is attributed to the increase in security concerns among organizations, the increasing number and complexity of cyber-attacks, the growing need for integrity & safety of confidential information in industry verticals, and the growing emphasis on compliance management. However, the data loss prevention segment is slated to register the highest CAGR during the forecast period due to the increasing regulatory and compliance requirements and the growing need to address data-related threats, including the risks of accidental data loss and exposure of sensitive data in organizations.

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Based on industry vertical, the market is segmented into government & defense, retail, manufacturing, banking, financial services, and insurance (BFSI), automotive & transportation, healthcare, IT & telecommunication, aerospace, education, and energy. In 2022, the IT & telecommunication sector is estimated to account for the largest share of the AI in cybersecurity market. The large share of this segment is mainly attributed to increasing incidence of security breaches by cybercriminal, shifting preference from traditional business models to sophisticated technologies, and including IoT devices, 5G, and cloud computing. However, the healthcare sector is slated to register the highest CAGR during the forecast period due to the rising sophistication levels of cyber-attacks, the growing incorporation of advanced cybersecurity solutions, the exponential rise in healthcare data breaches, and the growing adoption of IoT & connected devices across the healthcare sector.

Based on deployment, the market is segmented into on-premises and cloud-based. In 2022, the on-premises segment is estimated to account for the largest share of the artificial intelligence in cybersecurity market. The large share of this segment is attributed to the increasing necessity for enhancing the internal processes & systems, security issues related to cloud-based deployments, and the rising demand for advanced security application software among industry verticals. However, the cloud-based segment is slated to register the highest CAGR during the forecast period due to the increasing number of large enterprises using cloud platforms for data repositories and the growing demand to reduce the capital investment required to implement cybersecurity solutions. In addition, several organizations are moving operations to the cloud, leading cybersecurity vendors to develop cloud-based solutions.

Based on geography, in 2022, North America is estimated to account for the largest share of the overall artificial intelligence in cybersecurity market. The large market share of North America is attributed to the presence of major players along with several emerging startups in the region, the increase in government initiatives towards advanced technologies, such as artificial intelligence, the proliferation of cloud-based solutions, the increasing sophistication in cyber-attacks, and the emergence of disruptive digital technologies. However, Asia-Pacific is expected to register the highest CAGR during the forecast period. Factors such as the rising number of connected devices, the increasing privacy & security concerns, the growing awareness regarding cybersecurity among organizations, rapid economic development, high adoption of advanced technologies, such as IoT, 5G technology, and cloud computing are contributing to the growth of this market in Asia-Pacific.

The report also includes an extensive assessment of the key strategic developments adopted by the leading market participants in the industry over the past four years (20192022). The artificial intelligence in cybersecurity market has witnessed several partnerships & agreements in recent years that enabled companies to broaden their product portfolios, advance the capabilities of existing products, and gain cost leadership in the cybersecurity market. For instance, in 2021, Juniper Networks, Inc. (U.S.) launched Juniper Cloud Workload Protection, a software designed to automatically defend application workloads in any cloud or on-premises data center environment against application exploits in real-time. Similarly, in 2021, SecurityBridge (Germany) partnered with Fortinet, Inc. (U.S.) to address the security challenges posed by vulnerabilities within the SAP landscape. Also, in 2021, Check Point Software Technologies Ltd. (Israel) launched security gateways to protect SMBs against threats.

The global artificial intelligence in cybersecurity market is fragmented in nature. The major players operating in this market are Amazon Web Services, Inc. (U.S.), IBM Corporation (U.S.), Intel Corporation (U.S.), Microsoft Corporation (U.S.), Nvidia Corporation (U.S.), FireEye, Inc. (U.S.), Palo Alto Networks, Inc. (U.S.), Juniper Networks, Inc. (U.S.), Fortinet, Inc. (U.S.), Cisco Systems, Inc. (U.S.), Micron Technology, Inc. (U.S.), Check Point Software Technologies Ltd. (U.S.), Imperva (U.S.), McAfee LLC (U.S.), LogRhythm, Inc. (U.S.), Sophos Ltd. (U.S.), NortonLifeLock Inc. (U.S.), and Crowdstrike Holdings, Inc. (U.S.).

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Scope of the Report:

AI in CybersecurityMarket by Component

AI in CybersecurityMarket by Technology

AI in CybersecurityMarket by Security Type

AI in CybersecurityMarket by Application

AI in Cybersecurity Market by Deployment Type

AI in CybersecurityMarket by Industry Vertical

AI in CybersecurityMarket by Geography:

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Artificial Intelligence in Cybersecurity Market Worth $66.22 Billion by 2029 - Exclusive Report by Meticulous Research - GlobeNewswire

To the average person, it must seem as if the field of artificial intelligence is making immense progress. According to the press releases, and some of the more gushing media accounts, OpenAIs DALL-E 2 can seemingly create spectacular images from any text; another OpenAI system called GPT-3 can talk about just about anything; and a system called Gato that was released in May by DeepMind, a division of Alphabet, seemingly worked well on every taskthe company could throw at it. One of DeepMinds high-level executives even went so far as to brag that in the quest for artificial general intelligence (AGI), AI that has the flexibility and resourcefulness of human intelligence, The Game is Over! And Elon Musk said recently that he would be surprised if we didnt have artificial general intelligence by 2029.

Dont be fooled. Machines may someday be as smart as people, and perhaps even smarter, but the game is far from over. There is still an immense amount of work to be done in making machines that truly can comprehend and reason about the world around them. What we really need right now is less posturing and more basic research.

To be sure, there are indeed some ways in which AI truly is making progresssynthetic images look more and more realistic, and speech recognition can often work in noisy environmentsbut we are still light-years away from general purpose, human-level AI that can understand the true meanings of articles and videos, or deal with unexpected obstacles and interruptions. We are still stuck on precisely the same challenges that academic scientists (including myself) having been pointing out for years: getting AI to be reliable and getting it to cope with unusual circumstances.

Take the recently celebrated Gato, an alleged jack of all trades, and how it captioned an image of a pitcher hurling a baseball. The system returned three different answers: A baseball player pitching a ball on top of a baseball field, A man throwing a baseball at a pitcher on a baseball field and A baseball player at bat and a catcher in the dirt during a baseball game. The first response is correct, but the other two answers include hallucinations of other players that arent seen in the image. The system has no idea what is actually in the picture as opposed to what is typical of roughly similar images. Any baseball fan would recognize that this was the pitcher who has just thrown the ball, and not the other way aroundand although we expect that a catcher and a batter are nearby, they obviously do not appear in the image.

A baseball player pitching a ballon top of a baseball field.A man throwing a baseball at apitcher on a baseball field.A baseball player at bat and acatcher in the dirt during abaseball game

Likewise, DALL-E 2 couldnt tell the difference between a red cube on top of a blue cube and a blue cube on top of a red cube. A newer version of the system, released in May, couldnt tell the difference between an astronaut riding a horse and a horse riding an astronaut.

When systems like DALL-E make mistakes, the result is amusing, but other AI errors create serious problems. To take another example, a Tesla on autopilot recently drove directly towards a human worker carrying a stop sign in the middle of the road, only slowing down when the human driver intervened. The system could recognize humans on their own (as they appeared in the training data) and stop signs in their usual locations (again as they appeared in the trained images), but failed to slow down when confronted by the unusual combination of the two, which put the stop sign in a new and unusual position.

Unfortunately, the fact that these systems still fail to be reliable and struggle with novel circumstances is usually buried in the fine print. Gato worked well on all the tasks DeepMind reported, but rarely as well as other contemporary systems. GPT-3 often creates fluent prose but still struggles with basic arithmetic, and it has so little grip on reality it is prone to creating sentences like Some experts believe that the act of eating a sock helps the brain to come out of its altered state as a result of meditation, when no expert ever said any such thing. A cursory look at recent headlines wouldnt tell you about any of these problems.

The subplot here is that the biggest teams of researchers in AI are no longer to be found in the academy, where peer review used to be coin of the realm, but in corporations. And corporations, unlike universities, have no incentive to play fair. Rather than submitting their splashy new papers to academic scrutiny, they have taken to publication by press release, seducing journalists and sidestepping the peer review process. We know only what the companies want us to know.

In the software industry, theres a word for this kind of strategy: demoware, software designed to look good for a demo, but not necessarily good enough for the real world. Often, demoware becomes vaporware, announced for shock and awe in order to discourage competitors, but never released at all.

Chickens do tend to come home to roost though, eventually. Cold fusion may have sounded great, but you still cant get it at the mall. The cost in AI is likely to be a winter of deflated expectations. Too many products, like driverless cars, automated radiologists and all-purpose digital agents, have been demoed, publicizedand never delivered. For now, the investment dollars keep coming in on promise (who wouldnt like a self-driving car?), but if the core problems of reliability and coping with outliers are not resolved, investment will dry up. We will be left with powerful deepfakes, enormous networks that emit immense amounts of carbon, and solid advances in machine translation, speech recognition and object recognition, but too little else to show for all the premature hype.

Deep learning has advanced the ability of machines to recognize patterns in data, but it has three major flaws. The patterns that it learns are, ironically, superficial, not conceptual; the results it creates are difficult to interpret; and the results are difficult to use in the context of other processes, such as memory and reasoning. As Harvard computer scientist Les Valiant noted, The central challenge [going forward] is to unify the formulation of learning and reasoning. You cant deal with a person carrying a stop sign if you dont really understand what a stop sign even is.

For now, we are trapped in a local minimum in which companies pursue benchmarks, rather than foundational ideas, eking out small improvements with the technologies they already have rather than pausing to ask more fundamental questions. Instead of pursuing flashy straight-to-the-media demos, we need more people asking basic questions about how to build systems that can learn and reason at the same time. Instead, current engineering practice is far ahead of scientific skills, working harder to use tools that arent fully understood than to develop new tools and a clearer theoretical ground. This is why basic research remains crucial.

That a large part of the AI research community (like those that shout Game Over) doesnt even see that is, well, heartbreaking.

Imagine if some extraterrestrial studied all human interaction only by looking down at shadows on the ground, noticing, to its credit, that some shadows are bigger than others, and that all shadows disappear at night, and maybe even noticing that the shadows regularly grew and shrank at certain periodic intervalswithout ever looking up to see the sun or recognizing the three-dimensional world above.

Its time for artificial intelligence researchers to look up. We cant solve AI with PR alone.

This is an opinion and analysis article, and the views expressed by the author or authors are not necessarily those of Scientific American.

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Artificial General Intelligence Is Not as Imminent as You Might Think - Scientific American