Monthly Archives: January 2021

Dublin, Jan. 15, 2021 (GLOBE NEWSWIRE) -- The "Artificial Intelligence in Next Generation Networking by Infrastructure, Network Type, IoT Solution, Segment (Consumer, Enterprise, Industrial, and Government), and Industry Verticals 2021-2026" report has been added to ResearchAndMarkets.com's offering.

This report assesses the impact of AI in various networking products and solutions including embedded equipment, components, and software platforms (network automation, optimization, and transformation). The report also evaluates the role of SDN, Edge Computing, NFV, and Augmented Intelligence in the formation and support of AI driven networking ecosystems.

In addition, the impact of 5G networks, IoT technology and systems, and network analytics functions is also analyzed. The report assesses technologies, products, and solutions from key solution providers, identifying key companies in each segment of the competitive landscape.

The report also provides forecasts for the AI-driven networking market based on major market segments and subsegments, AI technology type, deployment type, network type, industry vertical and region. The report provides qualitative and quantitative analysis for AI in Next Generation Networking by Infrastructure, Network Type, IoT Solution, Segment (Consumer, Enterprise, Industrial, and Government), and Industry Vertical from 2021 through 2026.

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Artificial Intelligence is rapidly becoming an integral part of various digital technologies including software and many aspects of ICT infrastructure. For example, the AI chipset marketplace is poised to transform the entire embedded system ecosystem with a multitude of AI capabilities such as deep machine learning, image detection, and many others. With 85% of all chipsets globally shipping AI-equipped, over 63% of all electronics will have some form of embedded intelligence by 2026.

Infrastructure is anticipated to be one of the primary focus areas for AI as network operators seek to reduce costs and improve efficiencies while simultaneously reducing the incidence of errors and adverse network events. AI technology will play a key role in the transformation of network intelligence to become increasingly self-driven. Technologies like cognitive computing, machine learning, deep learning, and predictive application will be fundamental to the transformation of network configuration automation and operational autonomy.

AI-driven networking is going to impact wireless networking of all sizes for all communication service providers, improving service realization and support, and ultimately impacting every industry vertical from transportation to medical care to financial services. Furthermore, the analyst sees the convergence of AI and Internet of Things (IoT) technologies and solutions (AIoT) leading to "thinking" networks and systems that are becoming increasingly more capable of solving a wide range of problems across a diverse number of industry verticals.

In terms of the impact of AI on wireless networks, the evolution is already underway from a standards and network topology approach with 5G service-based architectures. Implementation within public communications service providers will scale slowly due to legacy systems such as OSS/BSS. However, closed-loop private 5G wireless networks will be in the vanguard of AI network deployment.

This evolution will lead to AI-enabled functions throughout 6G networks within the 2030 to 2040 timeframe. This will include contextually agile RF networks that support event-driven adaptation and resource allocation optimization. It will also include many improvements at the device level such as AI-enabled distributed computing, which will facilitate persistent computation-oriented communications.

Target Audience:

Report Benefits:

Key Topics Covered:

1.0 Executive Summary

2.0 Introduction2.1 Unified Networks involving AI and IoT2.2 AI Driven Networks2.3 AI in Wireless Network Strategy2.4 Intent Based Networks2.5 Self-driven Networks2.6 Augmented Intelligence2.7 AI Technologies in Networking2.8 Market Drivers and Challenges

3.0 Technology and Application Analysis3.1 Networking Equipment, Platforms, and Services3.2 Enterprise Networking3.3 IoT Networking Technology3.3.1 Short Range Wireless Technologies3.3.2 Long Range Wireless Technologies3.4 IoT Application3.5 Network Optimization3.6 Network Automation3.7 Network Transformation3.8 Edge Computing and AI3.9 Software Driven Networking3.10 Software Defined Infrastructure3.11 AI-Defined Infrastructure3.12 Network Function Virtualization3.13 Telecom Infrastructure and Cloud RAN3.14 New Radio Technology and 5G Network3.15 AI Powered Network Analytics3.16 Competitive Landscape

4.0 Company Analysis4.1 Cisco Systems4.2 Hewlett Packard Enterprise4.3 IBM Corporation4.4 Samsung Electronics Co Ltd.4.5 Baidu Inc.4.6 Nvidia Corporation4.7 Google Inc.4.8 Microsoft Corporation4.9 Dell EMC4.10 Nokia Corporation4.11 ARM Limited4.12 Xively4.13 PTC Corporation4.14 Huawei Technologies Co. Ltd.4.15 ZTE Corporation4.16 Intel Corporation4.17 Ericsson AB4.18 Fujitsu Ltd.4.19 NEC Corporation4.20 H2O.ai4.21 Qualcomm Incorporated4.22 Juniper Networks, Inc.4.23 Accenture PLC4.24 Brocade Communication Systems4.25 VMware Inc.4.26 Aricent Inc.4.27 Arista Networks Inc.4.28 Extreme Networks4.29 NETSCOUT4.30 ECI Telecom4.31 Foxconn Electronics Inc.4.32 NETGEAR4.33 Riverbed Technology

5.0 Market Analysis and Forecast 2021 - 20265.1 Global AI Networking Solution Market 2021 - 20265.2 Global AI Networking Solution Market by Segment5.2.1 Global AI Networking Solution Market by Hardware5.2.1.1 Global AI Networking Solution Market by Hardware Equipment5.2.1.2 Global AI Networking Solution Market by Hardware Component5.2.2 Global AI Networking Solution Market by Software5.2.2.1 Global AI Networking Solution Market by Network Management Software5.2.2.1.1 Global AI Networking Solution Market by Network Automation Software5.2.2.1.2 Global AI Networking Solution Market by Network Optimization Software5.2.2.1.3 Global AI Networking Solution Market by Network Transformation Software5.2.3 Global AI Networking Solution Market by Service5.2.3.1 Global AI Networking Solution Market by Managed Service5.2.3.2 Global AI Networking Solution Market by Professional Service5.2.3.2.1 Global AI Networking Solution Market by Deployment, Integration, and Provisioning Service Type5.2.3.2.2 Global AI Networking Solution Market by Wireless Infrastructure Management Service Type5.2.3.2.2.1 Global AI Networking Solution Market by Telecom Infrastructure Management Service Type5.2.3.2.2.2 Global AI Networking Solution Market by 5G Infrastructure Management Service Type5.3 Global AI Networking Solution Market by AI Technology5.4 Global AI Networking Solution Market by Deployment5.5 Global AI Networking Solution Market by Industry5.6 Global AI Networking Solution Market by Organization5.7 Global AI Networking Solution Market by Network Type5.8 Global AI Networking Solution Market by Network Platform5.8.1 Global AI Networking Solution Market by IoT Network Technology Type5.8.1.1 Global AI Networking Solution Market by Short and Medium Range Wireless IoT Network Technology5.8.1.2 Global AI Networking Solution Market by Long Range Wireless IoT Network Technology5.8.2 Global AI Networking Solution Market by IoT Application Type5.9 Global AI Networking Solution Market by 5G Network Type5.10 Global AI Networking Solution Market by Industry Vertical5.11 Global AI Networking Solution Market by Region5.11.1 North America AI Networking Solution Market by Country5.11.2 Europe AI Networking Solution Market by Country5.11.3 APAC AI Networking Solution Market by Country5.11.4 MEA AI Networking Solution Market by Country5.11.5 Latin America AI Networking Solution Market by Country

6.0 Conclusions and Recommendations6.1 Artificial Intelligence Providers6.2 Broadband Infrastructure Providers6.3 Communication Service Providers6.4 Computing Companies6.5 Data Analytics Providers6.6 Networking Equipment Providers6.7 Networking Security Providers6.8 Semiconductor Companies6.9 IoT Suppliers and Service Providers6.10 Software and Platform Providers

For more information about this report visit https://www.researchandmarkets.com/r/64if4m

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Global Artificial Intelligence in Next Generation Networking Market (2021 to 2026) - by Infrastructure, Network Type, IoT Solution, Segment and...

January 14, 2021 Artificial intelligence will continue to gradually revolutionize productivity and functionality as long as policy concerns are addressed, said three separate panel of experts on Tuesday at CES 2021.

Various AI technologies will contribute $16 trillion to the economy by 2030. Better machines, better software, and an explosion of applications affecting everyday lives will continue. AI has enhanced productivity, improved safety, and made the world more accessible.

As AI does not replace but enhances human work, it presents an enormous set of curated data that must be entrusted with established guidelines and transparency.

IBM Vice President Bridget Karlin raised a concern involving ethics, and not building bias. Depending on companies purposes for using AI, models can be ethical when they are engineered to be fair and properly calibrated.

It is incumbent upon software developers to identify the requirements for ethical and non-biased data collection, she said.

What about AIs involvement in creating and spreading fake news? Kevin Guo, CEO of Hive, said this is a service still in process.

It is essential for AI engineers to research and implement data fairness and remove bias.

On health care, participants in a separate panel said AI leads to improved outcomes and lower costs. But to Christina Silcox, a digital health policy fellow, the question is: How can people trust something that cant be seen or understood?

Understanding how technologies are created helps, said Jesse Ehrenfeld from the Board of Trustees of American Medical Association. But he acknowledged that all data is in some way biased. He said he cant tell the number of times data flows have generated different meanings than expected.

Christina Silcox said that it was critical to understand how software will work overtime after being put into place.

Indeed, communication and transparency are key for trust and growth of AI, said Senior Regulatory Specialist Pat Baird. Depending on who the stakeholders are, there will need customization of such communication.

Trustworthy AI will re-humanize health care, letting computers do what they were built to be done, and allow the health care workers to work with people, he said.

Another panel during 2021 CES discussed gender and racial bias in a business setting and how AI can contribute show and reflect equal representation.

Annie Jean-Baptiste of Google said that humble inclusion fills innovation. Kimberly Sterling of ResMed declared that people are not going anywhere, and that AI will not replace peoples brains.

All three panel discussions pondered the future of AI. Most agreed that AI exists to supplement human ambition, enabling everyday businesses to become smarter, adjust to new inputs and perform human-like-tasks.

For richer data sets, one of the solutions that panelists proposed to break out the black box of AI, make sure to assemble those capabilities and understand the sources of the data, to go back and test the models, and to be able to look holistically at outcomes.

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Artificial Intelligence Aims to Enhance Human Capabilities, But Only With Caution and Safeguards - BroadbandBreakfast.com

Ive spent two decades as a researcher and educator in artificial intelligence, drawn to the field by the opportunity to explore the mysteries of perception and cognition. But life is rarely as simple as wed like, and the arc of my career has paralleled my mothers escalating health struggles, including a chronic, life-threatening cardiovascular condition. As all-consuming as the world of academia can be, it sometimes feels as if Ive spent as much time in hospitals as I have in my lab.

Im happy to report my mother continues to persevere, but her resilience hasnt been the only silver lining to this ordeal. Years spent in the company of nurses and doctorsunfailingly committed, but perpetually overworked and often sleep deprivedconvinced me that the power of AI could radically elevate the way care is delivered. Intelligent sensors could keep tireless watch over patients, automate time-consuming tasks like charting and transcription, and identify lapses in safety protocols as they happen. After all, if AI can safely guide cars along freeways at 70 miles per hour, I wondered, why cant it help caregivers keep up with the chaos of the healthcare environment?

At the heart of this idea was an obstacle, however. I was proposing research that extended beyond the limits of computer science and into an entirely different field, with decades of literature and traditions stretching back generations. It was clear I needed a collaboratornot just an authority in healthcare, but one with the patience and open-mindedness to help an outsider bring something new to the table. For the first time in my career, success would depend on more than the merits of my work; it would require the humility of researchers like me to recognize the boundaries of our knowledge, and the graciousness of experts in another discipline to help us overcome them.

Thankfully, luck was on my side. In 2012, a colleague introduced me to Arnie Milstein, a Stanford Medical School professor and member of the National Academy of Medicine with an interest in both the policy and the technology that drives healthcare. Our first real conversation on the topic turned a casual lunch at a Vietnamese pho restaurant into an impromptu, hours-long brainstorming session. The exuberance of that day never wore off, as we convened a coalition of researchers to explore the automated tracking of surgical tools during operations, privacy-preserving monitors that ensure the safety of high-risk patients and vulnerable seniors, and networks of smart sensors that help hospital staff maintain hand hygiene throughout their shifts. Finally, in September, after years of experimentation, refinements, and presentations at conferences all over the world, our research was published in Nature. And now, with the help of legal scholars, bioethicists, and even a philosopher, were partnering with select hospitals and senior homes to pilot its use in the hands of real caregivers.

The success of my collaboration with Professor Milstein demonstrates an important idea: AIs applications are vast, but technology will represent only part of any given breakthrough. The remainder will be found in the contributionseven leadershipof experts from a growing list of fields, of which healthcare is only one example. Similar partnerships await as AI intersects with economics, energy, environmental science, public health, education, and even the humanities.

For instance, its hard to talk about any application of technology in 2020 without addressing the coronavirus pandemic. This was among the motivating factors behind the launch of AI Cures, an MIT initiative that brings together researchers in machine learning and life sciences to accelerate the speed with which antivirals can be identified, evaluated, and ultimately deployed. Its applications in the face of COVID-19 are obvious, but its broader goal of elevating our defense against pathogens of all kinds will remain relevant long after the challenges of the present moment are behind us. In addition to its core research mission, the group has organized impressively inclusive events in recent months, providing a venue for presenters with backgrounds in computer science, infectious disease, cardiology, synthetic biology, and many others.

Similarly encouraging is the work of my colleague, Stanford law professor Dan Ho. His lab has published extensively on the utility of AI in the public sector, and is now working with the EPA to use machine learning to dramatically improve the tracking of ecological contamination at a national scale. The underlying technology is transformative, but its the involvement of legal scholars, policymakers, and government representatives that truly makes it applicable in the real world.

These stories are a testament to the power of humility, but the sheer scale of the challenges that remain calls for a more organized response. It was with this in mind that I partnered with Stanford professor of philosophy and former provost John Etchemendy to co-found the Stanford Institute for Human-Centered Artificial Intelligence, or HAI, in 2018. Its ongoing mission is to reframe the pursuit of AI in unequivocally human terms, to reflect its dependence on interdisciplinary alliances, and to ensure ethics, compassion, and societal responsibility are baked in from the earliest stages of our workwhether its an algorithm, a commercial product, or even legislation.

HAIs reach as an institution is helping to cross new divides as well, beyond those that separate academic worlds. Partnerships with corporations, governments, and NGOs, for instance, will be essential in building a larger community around these values. Already, for example, theyve helped us organize cross-disciplinary workshops that bring ethical, philosophical, and legal expertise to bear on contentious technologies like facial recognition, with audiences of executives and legislators at both the state and federal level. And our relationships with tech leaders like Google and Amazon allow us to offer powerful cloud computing accessa foundational but often cost-prohibitive resource for modern AI researchto young, innovative thinkers in the form of grants.

Ultimately, however, this appreciation for the power of humilityopenness, transparency, and a reverence for the expertise of otherscant be mandated from the top down. It must be built up from a cultural level, and thus requires an investment in educational efforts to instill them in the next generation of AI practitioners. Here at Stanford, political science professor Rob Reich co-created a course in the computer science department entitled Computers, Ethics and Public Policy, intended to augment the education of engineers with an awareness of their impact on people and communities, while Harvard computer science professor Barbar Grosz explores similar issues in a course called Embedded Ethics. These are encouraging signs of a shift in the way we educate not just tomorrows technologists, but business leaders, social scientists, and politicians. Its my hope that universities across the world will be inspired to follow suit.

The excitement and anxiety surrounding AI can lend it a fatalistic tone, with aggressive language like revolution, tectonic shift, and force for change all too common. But while it might seem inevitable that AI will reshape the world, collaborations like these are a chance for the worldin all its messy, complicated vibrancyto reshape AI in turn. So although Im continually excited by what were learning about intelligent machines, Im even more excited by what we can learn from each other. All it takes is the willingness to ask, and that great, understated strengthour humility.

Dr. Fei-Fei Li is the Sequoia Professor, Computer Science Department, and Denning Codirector, Stanford Institute for Human-Centered Artificial Intelligence, Stanford University. She is an elected Member of the National Academy of Engineering, and the National Academy of Medicine.

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Stanford AI scholar Fei-Fei Li writes about humility in tech - Fast Company

Since AI has become a popular technology in the current industry, Artificial Intelligence vs. Human Intelligence has led to debates.

Artificial Intelligence has come a long way from being a part of science fiction to reality. Nowadays we are equipped with many intelligent devices, including self-driving vehicles, intelligent virtual aides, chatbots, and surgical robots. Since AI has become a popular technology in the current industry and a part of the everyday life of the common man, Artificial Intelligence vs. Human Intelligence has led to debates.

Artificial Intelligence is a Computer Science Branch that aims at developing intelligent machines that carry out a broad variety of tasks, typically involving human intelligence and expertise. These smart machines are based on experience and historical information, assessing their environment, and conducting appropriate activities.

Human Intelligence refers to the cognitive capacity of human beings that enables us to think, gain from various experiences, comprehend abstract concepts, apply logic and rationality, solve problems, identify patterns, make observations and choices, retain knowledge, and interact with fellow humans. It is supported by abstract emotions such as self-confidence, enthusiasm, and motivation to enable humans to perform complex tasks.

The pace of implementation A doctor could make a diagnosis in around ten minutes, but a million could be made by the AI system simultaneously.

Less partial There are no partial views on decision making.

Operational capacity-They dont foresee an end to their work due to saturation

Accuracy-The specificity of the outcomes clearly enhances

In many activities, artificial intelligence is critical, in particular when it comes to tedious decisions.

Human Intelligence and pace of AI

Computers can handle more data at a faster rate, as opposed to humans. For instance, AI can solve 10 issues in a minute if the human mind can solve a mathematical problem in 5 minutes.

Making Decisions

In decision making, AI is highly analytical as it assesses based on strictly collected data. The judgments of humans, however, can be affected by individual traits that are not based on statistics itself.

Multiple roles

Human knowledge supports the multifunctional mission, as proved by separate and simultaneous functions, while AI can perform fewer tasks only when a machine can learn duties one by one.

Social interacting

As social beings, people can process abstract knowledge, become self-confident and sensitive to others feelings, and can interact much better. On the other hand, AI has not developed its ability to collect valuable social and emotional knowledge.

According to Pew Research Center, Experts say the rise of artificial intelligence will make most people better off over the next decade, but many have concerns about how advances in AI will affect what it means to be human, to be productive, and to exercise free will.

One example among others that they mentioned, Marina Gorbis, executive director of the Institute for the Future, said, Without significant changes in our political economy and data governance regimes, AI is likely to create greater economic inequalities, more surveillance and more programmed and non-human-centric interactions. Every time we program our environments, we end up programming ourselves and our interactions. Humans have to become more standardized, removing serendipity and ambiguity from our interactions. And this ambiguity and complexity is what is the essence of being human.

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Impact of Artificial Intelligence vs Humans and What the Future Holds - Analytics Insight

To drive American leadership in the field of AI into the future, the National Artificial Intelligence Initiative Office has been launched by the White House Office of Science and Technology Policy (OSTP).

The new agency was established under the American Artificial Intelligence Initiative Act of 2020, which was enacted and codified into law to expand many existing AI policies and initiatives throughout the federal government.

The nascentNational Artificial Intelligence Initiative Officeis charged with overseeing and implementing Americas national AI strategy, according to a statement by the White House. It will work to provide federal coordination and collaboration in AI research and policymaking across the government, as well as with private sector, academia and other stakeholders.

The National Artificial Intelligence Initiative Office will be integral to the federal governments AI efforts for many years to come, serving as a central hub for national AI research and policy for the entire U.S. innovation ecosystem,Michael Kratsios, the nations chief technology officer, said in astatementto The Hill. Kratsios is the nations fourth CTO since theoffice was created in 2009under President Barack Obama.

TheAmerican Artificial Intelligence Initiative, which was established in February 2019, identified five central goals for the nations AI direction, including increasing AI research investment, releasing federal AI computing and data resources, setting AI technical standards, building Americas AI workforce and engaging with international allies.

In addition, theSelect Committee on Artificial Intelligence, which was launched by the White House in 2018 to coordinate Federal AI efforts, is being expanded and made permanent, according to the White House. The committee will serve as the senior interagency body responsible for overseeing the National AI Initiative.

Important related efforts in the nations AI strategy were unveiled last August and September when a series of national AI research institutes were announced by the National Science Foundation.

In August of 2020,five new NSF AI instituteswere created at a cost of $100 million to expand AI to a broader range of businesses across the U.S. economy. The initiatives aim to deepen the NSFsartificial intelligenceresearch to expand the nations workforce and drive new possibilities for a wide range of businesses, educational institutions, medicine, banking and other organizations.

Those AI institutes included the NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography,led by a team at the University of Oklahoma; the NSF AI Institute for Foundations of Machine Learning,led by a team at the University of Texas; the NSF AI Institute for Student-AI Teaming,led by a team at the University of Colorado; the NSF AI Institute for Molecular Discovery, Synthetic Strategy, and Manufacturing (or the NSF Molecule Maker Lab),led by a team at the University of Illinois; and the NSF AI Institute for Artificial Intelligence and Fundamental Interactions,led by a team at the Massachusetts Institute of Technology.

Two related AI research institutes are also being created by theU.S. Department of Agricultureover the next five years using $40 million in funding to expand AI research in farming and food processing. They are the USDA-NIFA AI Institute for Next Generation Food Systems,led by a team at the University of California; and the USDA-NIFA AI Institute for Future Agricultural Resilience, Management, and Sustainability at the University of Illinois.

In September,eight additional NSF AI institutes were unveiledin partnership with Amazon, Google, Intel and Accenture. Those companies are contributing toward a $160 million partnership to fund the eight AI Research Institutes scheduled for creation in late 2021 by the National Science Foundation.This effort marked the first time in which direct industry funding for the AI institutes will be received by the NSF, which funded prior AI institutes on its own or with other governmental partners. Companies have participated in the NSF AI research institutes in the past with researchers, materials, content and more, but previously did not make direct monetary contributions.

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Honing In on AI, US Launches National Artificial Intelligence Initiative Office - HPCwire

Its no surprise that artificial intelligence is a key ingredient in the modern tech space. From machine learning to wearables to robotics, the AI across industries is a growing necessity for businesses looking to remain competitive in the long term. Yet there are a few common reasons why businesses often fall short in their AI strategy implementation.

Information for this eWEEK Data Points article was supplied by Dr. Charla Griffy-Brown, Professor of Information Systems and Technology Management, and Associate Dean of Executive and Part-Time Programs at Pepperdine Universitys Graziadio School of Business. Here she discusses five key reasons AI strategies fail and what businesses can do to avoid these pitfalls.

Early work on AI solutions usually involves small subsets of data, which require smaller computing resources. When AI expands into broader production systems, performance can be impacted exponentially. Insufficient attention to performance at scale creates AI systems that appear to work well during testing but quickly become unusable by the business at large.

Solution: Businesses should be accurate in computing requirements for scaling up and test, as often as possible, in a near-production environment.

There are fundamental issues that arise from decisions regarding data architecture. The wrong database can easily render a scaled AI working test system unusable. Furthermore, this is enhanced by data cleansing and preparation problems. For example, manual interventions by humans might be effective in preparing test data, but this typically cannot be scaled.

Solution: Make data architecture decisions based on not just growth but an understanding of the processes required for the data training required to build AI.

One of the biggest challenges facing implementation of new technology is human beings, and AI implementation will only be as strong as the training and support for the staff implementing it. AI solutions must also be developed with a mechanism for ensuring customer facing channels are fully prepared for customer reactions. For example, this could include a temporary spike in phone calls if chatbots arent working properly or a tsunami of emails if a phone answering service isnt getting them where they need to go.

Solution: Realizing that AI requires human work is fundamental to thinking through AI deployment. Businesses will need to implement strategies to address challenges quickly in advance of an AI initiative, including considerations for how it will impact human staff and customers.

Supporting business issues that didnt appear in testing is very challenging to scale. Scaling AI requires production systems to allow for situations not in designs or plans. Over time, new challenges may arise because of changes in the AI system itself. Machine learning is designed to improve itself over time, and usually this improves the accuracy of an algorithm. However, it can also lead to other revelations, such as identifying new patterns of customer behavior or fraud.

Solution: An important part of scaling AI means developing and working through a variety of hypothetical scenarios. Businesses should develop technical and operational contingencies, such as asking how to switch off an AI solution temporarily with minimal disruption.

One of the most important problems in scaling AI for production are the security implications. Cyber risk is an element that has to be considered from all angles when deploying AI. AI introduces new vulnerabilities and represents new risks to established cybersecurity solutions.

Solution: Before deploying AI, companies should develop a risk-based approach to implementation, identifying any points of weakness and reinforcing these appropriately. They may also consider working with a third party to test cybersecurity protections ahead of time to identify points of vulnerability.

If you have a suggestion for an eWEEK Data Points article, email [emailprotected].

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Reasons Why AI Projects Fail, and How to Fix Them - eWeek