WINTERTHUR, Switzerland October 6, 2022 The Rieter Group is constantly expanding its technology leadership. Together with the Johann Jacob Rieter Foundation, the company is therefore supporting a new Endowed Professorship for Industrial Artificial Intelligence (AI) at the Zurich University of Applied Sciences (ZHAW) School of Engineering. The Professorship is dedicated to teaching and research in the field of industrial applications of Artificial Intelligence and will be announced later this year.

The new Endowed Professorship will be established at the Center for Artificial Intelligence (CAI) of the ZHAW in Winterthur. It will focus, in particular, on the application of machine learning methods and knowledge-based systems in connection with processes in production and service. The use of artificial intelligence in industry is becoming increasingly important, especially with regard to the potential of data for evaluation and control of complex processes. The support of the Johann Jacob Rieter Foundation and the Rieter Group will allow us to further expand AI research in the field of industrial applications, explains Prof. Dr. Dirk Wilhelm, Director of the ZHAW School of Engineering.

For Rieter, the commitment is related to the implementation of its technology leadership strategy. The use of Artificial Intelligence will make a significant contribution to automation and process optimization, and thereby advance sustainability in the textile industry. This makes it an important element of the leading technology that Rieter offers, emphasizes Rieter CEO Dr. Norbert Klapper.

The contribution of the Johann Jacob Rieter Foundation to sponsoring the Professorship is in line with the Winterthur Cluster Initiative. The increasing digitalization of production processes opens up new perspectives for Winterthur as a business location. The Smart Machines cluster is growing in importance, says Thomas Anwander, member of the Foundation Board, and adds: The Endowed Professorship for Industrial AI at the ZHAW aims to promote Winterthur as a technology location by pooling locally available strengths in mechanical engineering and Industry 4.0.

The Endowed Professorship will serve to build expertise in the field of Industrial AI and will oversee a group that will focus on teaching and research pertaining to trustworthy machine learning. This involves, for example, the deployment of artificial intelligence with the aim of optimizing production processes in relation to the use of raw materials and energy, and making expert knowledge more readily available.

In addition to research, for the purpose of knowledge transfer, the new professorship will also be active in teaching, in the bachelors degree programs in Computer Science and in Data Science, in the Master of Science in Engineering, and in continuing education.

The annual commitment of CHF 300 000 over a period of six years will be financed equally by the Rieter Group and the Johann Jacob Rieter Foundation.

Posted: October 6, 2022

Source: The Rieter Group

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Rieter And The Johann Jacob Rieter Foundation Sponsor Professorship For Artificial Intelligence At The Zurich University of Applied Sciences (ZHAW) -...

Violin-shaped buildings in the style of Gaudi produced by AI art tool Midjourney, from a prompt by Evrim Yazgin.

AI art tools like Craiyon (formerly DALL-E mini) and Midjourney have been making waves on the internet over recent months. But are these artificial intelligence tools exhibiting creativity, or just clever mimics? How will AI art impact artists themselves? And how can machine learning be effectively used in artistic, creative and design endeavours most effectively?

Cosmos science journalist Evrim Yazgin tackles these questions and speaks with AI expert Professor Jon McCormack, Director of Monash Universitys SensiLab, in the article Creativity and AI in Cosmos Magazine #96.

At this years Colorado State Fairs annual art competition awarded its prize to an AI-generated piece entitled Thtre Dopra Spatial by Jason M. Allen. But Allen didnt paint or sculpt the work it was generated by AI art tool Midjourney.

While Allen was open about the pieces origins, submitting it under the name Jason M. Allen via Midjourney, that the prize was awarded to a piece created by a machine learning tool has enraged some artists.

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Impressive and controversial as tools like Midjourney are, they represent only the tip of the AI art iceberg.

AI expert Professor Jon McCormack, Director of Monash Universitys SensiLab says AI should not be thought of as intelligent, creative or artistic. These systems are kind of statistical mirrors on broad aspects of human culture, McCormack explains.

McCormack stresses that AI is best used as an aid to human creativity.

In Cosmos Magazine #96 McCormack shows Evrim Yazgin around SensiLab, where AI researchers are creating design and art pieces. Of note are a mirror which writes a poem based on the onlookers expression, a machine which can learn from human drummers how to make a beat, and a tapestry drawing on the stories of female convicts in Australia.

Cosmos Magazine #96 is available now at all good newsagents or Subscribe at comosmagazine.com and save up to $35.

Cosmos is a quarterly science magazine. We aim to inspire curiosity in The Science of Everything and make the world of science accessible to everyone.

Theres never been a more important time to explain the facts, cherish evidence-based knowledge and to showcase the latest scientific, technological and engineering breakthroughs. Cosmos is published by The Royal Institution of Australia, a charity dedicated to connecting people with the world of science. Financial contributions, however big or small, help us provide access to trusted science information at a time when the world needs it most. Please support us by making a donation or purchasing a subscription today.

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AI Art: Proof that AI is creative? - Cosmos

CSU professor says artificial intelligence can be good atmospheric science teachers.

FORT COLLINS, Colo. The science of weather prediction improves every year but there are still so many mysteries to solve.

Colorado State University (CSU) professor Elizabeth Barnes believes that some of those answers might come from artificial intelligence (AI) also known as machine learning. Essentially thats when a computer program makes a prediction based on patterns that it finds in huge amounts of data.

"It can sort through data much faster than we can and in most cases it can also do it better," said Barnes. "And sometimes it might even find relationships that we didn't know were there. We can learn new science."

Barnes said she is often impressed with the accuracy of an AI-driven climate forecast but she is more interested in learning how the machine got that answer in the first place.

What Barnes and her collages are working on at CSU is called Explainable Artificial Intelligence (XAI). Barnes said it's like cracking the lid of the so called black box that seals the methods behind the machine.

We take that forecast or that prediction, and the idea is that you push that information back through your machine learning model," said Barnes. "And it gives you a map of what was important for it to make its decision. What were the ingredients it used.

Barnes said that road map of information has already led to a new understanding of how the ocean conditions impact long-range weather more than a month in advance.

"It's also helping us learn more about our climate models," Barnes said "In the insides of the models, pieces are actually being replaced with machine learning algorithms to do a better job."

Barnes said one of the beauties of machine learning is that you can keep the rules very simple and can almost use any type of data, even maps, words and images instead of just numbers and statistics.

It's a straight data driven approach to prediction modeling; AI doesn't need any equations to find a solution. Unlike numerical weather forecast models which are a more physical approach. Those models use things like Newtonian and Thermodynamic equations to make a weather prediction.

Machine learning tools allow us to be creative about how we do science," said Barnes. "This has allowed me to think about how I ask questions and what kinds of questions I ask, without barriers in the way I think a lot of climate science had in the past.

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Far from being a futuristic concept relegated to the realms of science fiction, the use of artificial intelligence (AI) in the workplace is becoming more common. The benefits of using AI are often cited by reference to time and productivity savings. However, the challenges of implementing AI into HR practice and procedures should not be underestimated.

AI technologies are already being used across a broad range of industries, at every stage in the employment cycle. From recruitment to dismissal, their use has significant implications. In recent months, incidents at Meta, Estee Lauder and payment service company Xsolla have hit the headlines for utilising AI when dismissing employees.

All three companies used algorithms as part of their selection process. For Meta and Xsolla, the algorithms used analysed employee performance against key metrics to identify those who were unengaged and unproductive. These employees were subsequently dismissed.

Similarly, Estee Lauder used an algorithm when making three makeup artists redundant, which assessed employees during a video interview. The software measured the content of the womens answers and expressions during interview and evaluated the results against other data about their job performance.It led to their dismissal.

Where algorithms are used in place of human decision-making, they risk replicating and reflecting existing biases and inequalities in society.

An AI system is created by a variety of participants, from those writing the code, inputting the instructions, those supplying the dataset on which the AI system is trained and those managing the process. There is significant scope for bias to be introduced at each stage.

If, for example, a bias towards recruiting men is included in the dataset, or women are under-represented, this is likely to be replicated in the AI decision. The result is an AI system making decisions that reproduces inherent bias. If unaddressed, those biases can become exaggerated as the AI learns becoming more adept at differentiating using those biases.

To mitigate this risk, HR teams should test the technology with comparison between AI and human decisions looking for bias. This is only going to be effective in combating unconscious bias if the reviewers comprise a diverse group themselves. If bias is discovered, the algorithm can and should be changed.

AI systems are increasingly being viewed by employers as an efficient way of measuring staff performance. While AI may identify top performers based on key business metrics, they lack personal experience, emotional intelligence and the ability to form an opinion to shape decisions. There is a danger that low-performing staff could be disregarded solely on an assessment of metrics. Smart employees are likely to find ways to manipulate AI to their advantage in a way that might not be so easy without technology.

It is tempting to trust AI to limit legal risks by using it for decision-making. Superficially, this may be right, but the potential unintended consequences of any AI system could easily create a lack of transparency and bias equivalent to that of its human creators.

When AI systems are used, there is an obligation to consider how these might impact on fairness, accountability and transparency in the workplace.There is also a risk of employers exposing themselves to costly discrimination claims, particularly where the policy of using AI disadvantages an employee because of a protected characteristic (such as sex or race) and discriminatory decisions are made as a result.

Until AI develops to outperform humans in learning from mistakes or understanding the law, its use is unlikely to materially mitigate risk in the meantime.

Catherine Hawkes is a senior associate in the employment law team at RWK Goodman.

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Artificial intelligence in the workplace - ComputerWeekly.com

SAN FRANCISCO, Oct. 5, 2022 /PRNewswire/ --The global artificial intelligence in cybersecurity market size is estimated to reach USD 93.75 billion by 2030, expanding at a CAGR of 24.3% from 2022 to 2030, according to a new study by Grand View Research, Inc. An unprecedented spike in cyber incidents has fostered the demand for AI, cloud, and machine learning for seamless operations, data safety and prompt response to cyber threats. Some factors, such as soaring internet penetration, expanding footfall of connected devices, and escalating data protection concerns, have triggered the need for advanced cybersecurity solutions.

Grand View Research Logo

Key Industry Insights & Findings from the report:

In terms of type, the cloud security segment will contribute notably toward the global market in the wake of the rising prominence of AI and machine learning.

Based on vertical, the BFSI sector will exhibit profound demand for AI in cybersecurity to resist cyberattacks and prevent data leaks.

North America AI in cybersecurity market share will be pronounced due to threats to mobile devices and soaring penetration of IoT and 5G in the region.

Some major players, such as IBM Corporation, Cylance Inc. (BlackBerry), Acalvio Technologies, Inc, Intel Corporation, Amazon Web Services, Inc., FireEye, Inc., LexisNexis, Fortinet, Inc., Micron Technology, Inc., and Darktrace are poised to boost their portfolios in the coming years.

Read 200-page full market research report, "Artificial Intelligence In Cybersecurity Market Size, Share & Trends Analysis Report By Type (Cloud Security, Network Security), By Offering, By Technology, By Application, By Vertical, By Region, And Segment Forecasts, 2022 - 2030", published by Grand View Research.

Artificial Intelligence In Cybersecurity Market Growth & Trends

Artificial intelligence (AI) in cybersecurity has leveraged a faster response to breaches and propelled the efficiency of cyber analysts. AI is likely to be sought for vulnerability management, threat hunting, and boosting network security. In doing so, emphasis on natural language processing, machine learning, deep learning, and neural networks could gain ground during the assessment period. For instance, deep learning has become trendier to track transactions, logs, and real-time data to detect threats. AI is highly sought-after to secure cloud services and on-premises architecture and spot abnormal user behavior.

Story continues

Natural language processing could remain a value proposition to foster the penetration of AI technologies in cyberspace. The trend for natural language inference, sentiment analysis, and text summarization will bode well for major companies gearing to reinforce artificial intelligence in the cybersecurity market share. Prominently, NLP has received impetus for fake news detection, clickbait detection, and rumor detection. Leading companies are likely to bank on NLP to detect malicious language and domain names produced for phishing scams.

Stakeholders predict North America to witness investments galore, on the heels of the high footprint of connected devices, IoT, and 5G. Moreover, the possibility of DDoS attacks and the growing prominence of IoT-enabled activities have prompted major players to bank on cutting-edge technologies to deter cyber incidents. To illustrate, in August 2019, Microsoft was reported to have alleged Russian hackers using IoT devices to breach enterprise networks. Industry participants expect bullish investments in machine learning platforms, threat hunting, and advanced analytics. Besides, Microsoft Security blocked over 35.7 billion phishing and malicious emails and more than 9.6 billion malware threats in 2021.

The competitive landscape alludes to an increased emphasis on organic and inorganic growth strategies, including mergers & acquisitions, product offerings, technological advancements, collaborations, and innovations. For instance, in July 2022, Darktrace rolled out Darktrace PREVENT to assist organizations in pre-empting cyber-attacks. Meanwhile, in August 2022, it was reported that Thoma Bravo was contemplating acquiring Darktrace. In February 2019, BlackBerry completed the acquisition of Cylance to bolster its footprint in AI cybersecurity.

Artificial Intelligence In Cybersecurity Market Segmentation

Grand view research has segmented the global artificial intelligence in cybersecurity market in terms of type, offering, technology, application, vertical, and region:

AI In CybersecurityMarket - Type Outlook (Revenue, USD Billion, 2017 - 2030)

Network Security

Endpoint Security

Application Security

Cloud Security

AI In CybersecurityMarket - Offering Outlook (Revenue, USD Million, 2017 - 2030)

AI In Cybersecurity Market - Technology Outlook (Revenue, USD Billion, 2017 - 2030)

AI In CybersecurityMarket - Application Outlook (Revenue, USD Billion, 2017 - 2030)

Identity and Access Management

Risk and Compliance Management

Data Loss Prevention

Unified Threat Management

Fraud Detection/Anti-Fraud

Threat Intelligence

Others

AI In CybersecurityMarket - Vertical Outlook (Revenue, USD Billion, 2017 - 2030)

AI In CybersecurityMarket - Regional Outlook (Revenue, USD Billion, 2017 - 2030)

List of Key Players of Artificial Intelligence In Cybersecurity Market

Acalvio Technologies, Inc

Amazon Web Services, Inc.

Cylance Inc. (BlackBerry)

Darktrace

FireEye, Inc.

Fortinet, Inc.

IBM Corporation

Intel Corporation

LexisNexis

Micron Technology, Inc

Check out more related studies published by Grand View Research:

IT & Telecom Cyber Security Market - The global IT & telecom cyber security market size is expected to reach USD 82.64 billion by 2030, expanding at a CAGR of 12.1% from 2022 to 2030 according to a study conducted by Grand View Research, Inc. Expansion of telecom networks, emerging BYOD trend, and the emergence of 5G technology are anticipated to increase the network traffic and create loopholes, thereby increasing the risk of cyber-attacks.

Artificial Intelligence In Fintech Market - The global artificial intelligence in fintech market size is expected to reach USD 41.16 billion by 2030, growing at a CAGR of 16.5% from 2022 to 2030, according to a new report by Grand View Research, Inc. Artificial intelligence (AI) is widely used in financial organizations to improvise their precision levels, enhance their efficiency and instant query resolving through digital banking channels. AI technology like machine learning can help organizations raise their value by improving loan underwriting and eliminating financial risk.

Artificial Intelligence Market - The global artificial intelligence market size is expected to reach USD 1,811.8 billion by 2030, according to a new report by Grand View Research, Inc. The market is anticipated to expand at a CAGR of 38.1% from 2022 to 2030. Artificial Intelligence (AI) denotes the concept and development of computing systems capable of performing tasks customarily requiring human assistance, such as decision-making, speech recognition, visual perception, and language translation.

Browse through Grand View Research's Next Generation Technologies Industry Research Reports.

About Grand View Research

Grand View Research, U.S.-based market research and consulting company, provides syndicated as well as customized research reports and consulting services. Registered in California and headquartered in San Francisco, the company comprises over 425 analysts and consultants, adding more than 1200 market research reports to its vast database each year. These reports offer in-depth analysis on 46 industries across 25 major countries worldwide. With the help of an interactive market intelligence platform, Grand View Research Helps Fortune 500 companies and renowned academic institutes understand the global and regional business environment and gauge the opportunities that lie ahead.

Contact:Sherry JamesCorporate Sales Specialist, USAGrand View Research, Inc.Phone: 1-415-349-0058Toll Free: 1-888-202-9519Email: sales@grandviewresearch.comWeb: https://www.grandviewresearch.comGrand View Compass| Astra ESG SolutionsFollow Us: LinkedIn | Twitter

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Artificial Intelligence (AI) in Cybersecurity Market to be Worth $93.75 Billion by 2030: Grand View Research, Inc. - Yahoo Finance

Danny Olivas

John Daniel Danny Olivas, former NASA astronaut and current member of the NASA Advisory Council, has joined the staff of the Visual Intelligence and Multimedia Analytics Laboratory (VIMAL) of USCs Information Sciences Institute (ISI) as Co-Director for AI Initiatives in Space.

Olivas brings considerable experience to VIMAL, ISI and USC. A veteran of space shuttle missions in 2007 and 2009, he is the recipient of two NASA Space Flight Medals and the NASA Exceptional Service and Exceptional Achievement Medals. Olivas completed five space walks totaling over 34 hours outside of the International Space Station. His expertise in space is rivaled only by his passion for it, and he brings both to his new role.

Olivas said, I am excited about the opportunity to help expand USCs footprint in space, for researchers and students alike.

VIMAL which is led by Wael AbdAlmageed, research director at ISI and research associate professor of electrical and computer engineering has a noble and straight-forward mission: to empower students to use artificial intelligence to make the world a better place, one day at a time. With Olivas on the team, VIMAL is now able to look beyond Earth to do this.

Olivas and AbdAlmageed met in July 2022, when they were both invited to Renaissance Weekend, a prestigious, non-partisan, invite-only retreat for innovative thinkers across disciplines held this year in Banff,Canada. They quickly realized they had a lot in common. AbdAlmageed said, Danny [Olivas] and I come from very similar mindsets and backgrounds. We believe in working hard on hard problems, without giving up, for a long period of time. We believe that is enough to make things happen.

Interested in each others area of expertise, the two discussed how to harness their collective grit and work together. A month later, Olivas visited ISI for the day to learn about VIMAL and ISI, and present his thoughts on opportunities for AI in space.

Olivas astronaut background and exposure to space provides a valuable new perspective on new areas for data analysis. Since the beginning of the [NASA] program, NASA has produced more data than has been analyzed, he noted. He added that this data is ripe for analysis through artificial intelligence and machine learning.

One application is climate, which AbdAlmageed called an area of growth for VIMAL.

NASA has instruments that can see things like water vapor in the upper atmosphere, Olivas said. All these kinds of things can not only be analyzed in their individual silos of monitoring, but you can now start to integrate data across the different instruments to build a much more robust picture of how climate changes are affecting a certain region.

Ozone and CO2 in the atmosphere, temperature trends across the planet, drought prediction, sea level detection, deforestation, planet population changes these are just some of the areas where NASA has useful historical data, according to Olivas.

This data has been available by NASA for many, many years, he added, and (they) provide an opportunity to take pieces of this information and start to integrate them together and allow computational technologies to take over where human beings have had to digest this information in the past, to try to make sense of it.

As NASA prepares for a future in which humans will travel to Mars, new and exciting AI applications will emerge. There are some specific robotic applications that are very unique to NASAs space program, Olivas said. For example, a robot that can check the mood of an astronaut based on their facial expressions or voice intonations something that will be increasingly important for mental health as missions extend from months to years with Mars exploration.

Exploration rovers are another area where Olivas sees room for more AI, again, with Mars as the example: It takes about twenty minutes to be able to send a command from Earth to Mars. By the time you get the photograph that your rover is marching over a cliff, its probably the wrong time to send the command to stop moving. So you want to have more intelligence being built on the platform to allow the rover to make decisions for itself.

Climate data, robots and rovers these are areas Olivas might take VIMAL in the future. However, the first problem Olivas and AbdAlmageed plan on tackling is trash, specifically orbital debris.

NASA defines orbital debris as any non-functional human-made object in orbit around the Earth. Think: spacecraft, satellites, rockets or what youd get if any of those collided or exploded. Debris ranges in size from sub-microns all the way to several meters from a paint chip to a school bus with hundreds of thousands of estimated pieces orbiting Earth.

The trouble comes from the fact that this debris travels at orbital velocities that are dangerous to NASAs missions picture that paint chip or school bus traveling at 16,000 mph! Olivas said, its that hyper-velocity impact that causes all sorts of problems, not only with the space station or human spacecraft, but also with satellite technology. It is a serious threat to astronauts, spacecraft and space exploration in general.

The VIMAL team will be looking at ways to use their sensing, computer vision and AI expertise to identify pieces of debris and track them for long periods of time as they orbit Earth.

Olivas extends a strong legacy of innovation in space exploration at USC. He joins fellow former NASA astronauts Paul Ronney and Garrett Reisman, who also serve as faculty at USC Viterbi, which is one of a core group of top schools with a distinct astronautical program. This wont be the first time Olivas and Reisman have worked together, they were classmates in the NASA astronaut program.

To date, school researchers have created innovations in spacecraft propulsion, space science, space environment, space communications, satellites and materials. Astronaut Neil Armstrong was a USC Viterbi alumnus, and the school has a dedicated Space Engineering Research Center at ISI.

USC Viterbi maintains strong connections with pioneering space organizations and alumni who design and build rockets and space launchers, communications and direct broadcasting satellites, navigational systems, crewed space vehicles and planetary probes.

At the end of the day, space is a human endeavor, said Olivas, who pointed out that part of being an astronaut involves looking out for one another. He seemed impressed by this aspect of the work done by VIMAL.

One thing that Ive come to appreciate at VIMAL is the inclusive nature of the collaborations; it is really inspirational, he said.

AbdAlmageed has very intentionally fostered the collaborative environment of VIMAL. Im proud that weve created a culture in the lab where everybody feels a sense of ownership and partnership. He continued, I couldnt have done something like hire Danny [Olivas] without the significant contributions of everyone in VIMAL who do the work day in and day out. This was a team effort. I am also very grateful to Dr. Craig Knoblock, ISI Executive Director, for supporting our ambitious initiatives and pursuits.

Olivas certainly seems excited to join the team, I look forward to sharing, learning and seeing where those opportunities might be with VIMAL, concluded the astronaut.

Published on October 3rd, 2022

Last updated on October 3rd, 2022

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Artificial Intelligence in Space - USC's Information Sciences Institute is on a Mission - USC Viterbi | School of Engineering - USC Viterbi School of...

Due to a relatively rapid shift in supply chains and related conditions, buying and selling critical materials isnt as easy as it used to be for businesses. In response, resident procurement teams and suppliers are going through a massive transformation to reap a more competitive advantage.

Faced with an unpredictable global economy, global manufacturers are tasked to manage risk better and prioritize digitalization, optimize MRO spend analysis, or implement a supplier intelligence solution as part of their procurement processes. These reformed goals mark a pronounced shift for procurement executives: transitioning their plans and strategies from purely tactical operations to a new strategic decision-making model, using AI-enabled technology. This will enable them to move closer to what Gartner IT has identified as autonomous procurement, which has the potential to drive efficiency and savings to new heights when the right building blocks are in place to help organizations compete faster and smarter.

Cognitive computing makes a difference. New AI/cloud-based technologies are significantly helping with data harmonizing and supply chain network architecture optimization in significant ways. These technologies can help procurement teams and their organizations to adapt to change by ensuring reaction times are quicker than their competition while bolstering supplier relationships. In addition, having real-time information helps them arrive at data-driven decisions faster and more reliably.

But managing this change is a tall order for any tech executive team. So lets dig into what is required and how to lay the groundwork for successful adoption and engagement.

Lets walk through some key areas of change management that your procurement team may undertake for prioritizing an internal digital transformation.

Leadership Alignment - Leadership in your organization must be flexible. Are they open to change management for procurement processes? Do they understand the benefits of AI technology? Can you get them on board to support upcoming changes?

Stakeholder Engagement - Stakeholders are essential in this process. Is your team ready to effectively engage with all the various stakeholders for whom some element of their behavior will be changed? For example, where is your Procurement Officer or CIO? Are they in the room with your team to make decisions on this?

Communication Practices - Throughout a change management scenario, your team will need to have transparent communication throughout the change. This communication must address the specific needs of procurement and MRO teams. This includes upstream communication to your supply base to minimize risk.

Training & Implementation - Change management at this level, as hinted at above, must include training for the new behavior. This training is not a one-size-fits-all approach; it must be customized for specific roles among the procurement teams.

Behavior Adoption - Teams undertaking this change must be able to define metrics that help those involved see the management changes transparently.

Variations on these themes may apply to your specific industry scenarios. But each of these is critical to company buy-in on your next moves.

A recent study by Globality showed that 90% of global procurement leaders are moving quickly to transform their operating models and processes to better meet the challenges of todays volatile, uncertain business world. Multiple data points from the study indicate this forward rush.

This advancement to transform procurement and operations models will help build agility and resilience in the ever-changing business world, said those executives surveyed.

The human element of the supply chain is a critical factor for change management in procurement. How can organizations capture human intelligence more effectively in procurement practices? Similarly, how can management teams and employees come to decisions on procurement operations? The short answers lie in the introduction of AI-enabled technology tools.

Labor shortages are helping fuel the rise of AI in operational manufacturing environments. In addition, as the baby boomer workforce ages out of manufacturing roles, fewer young people are entering into manufacturing and production fields. The result is that companies are inclined to look more closely at AI/ML technology tools to augment the workforce.

The burnout factor is real for procurement managers, who are said to be at their breaking point in procurement. A Ceridian 2022 Pulse of Talent survey in the UK found that UK workers suffered some form of burnout, either through deadline pressures (32%), higher workloads (49%), and even mental health difficulties (34%).

AI tools can augment human workers to avoid these burned-out periods and help drive greater employee satisfaction. AI can take over repetitive, menial human tasks that are more suited to automation. This, in turn, does not replace workers at their jobs but instead allows workers to take on other, more strategic, fulfilling work. Employees can work with management on making decisions on how to properly apply AI in a manufacturing or production environment to reduce costs and/or mitigate risks.

One of our customers, a leading manufacturer of tissue, pulp, paper, packaging building products and related chemicals, was struggling with bad data as their MRO inventory was inaccurate, which resulted in bad decision-making and significant delays.

The manufacturer needed help. The company wanted to work faster and have access to real-time and accurate decision-making. So our team came in to provide data analytics, artificial intelligence and visualization capabilities which enabled the manufacturer to optimize its asset strategies and inventory stock levels.

The team also brought change management principles and overall structure to the manufacturers supply ops, procurement, finance, and IT strategies. We aggregated multiple SAP/EAM systems data simultaneously. These AI strategies ensured that the right inventory was available at the right time. A verified savings of $20M+ was identified as a savings opportunity in the first 45 days.

AI-enabled technology can enable a procurement team to work more efficiently and effectively, helping quickly identify and manage supplier risks.

Its high time to streamline the procurement process, reduce costs, adapt quickly to change, and improve compliance with ever-changing policies and laws. Embracing change management and communicating top-down will help procurement teams and the entire organization adapt to change.

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Winds Of Change: Adapting To Procurement Change With Artificial Intelligence - Forbes

On September 22, 2022, the U.S. Patent and Trademark Office (USPTO) conducted a live meeting for its Artificial Intelligence (AI) and Emerging Technologies (ET) Partnership Series. During this meeting, panelists from industry and the USPTO provided helpful tips on drafting and prosecuting patent applications that include AI components, including special tips for the biotech industry.Key takeaways from the meeting and published materials will be summarized in our Three-Part Blog Series.

Part Two Landscape of AI in Biotechnology

Nicholas Pairolero, Research Economist, USPTO provided an informative landscape of AI in Biotech. Overall, AI is increasingly used in biotechnology, however biotechnology AI patenting is diffusing across all technologies, owners, and inventor-patentees. The definition of AI in this panel corresponded with 8 component technologies, including planning/control, knowledge processing, speech, AI hardware, evolutionary computation, natural language processing, machine learning, and vision. Some interesting charts generated by Mr. Pairolero and presented during the panel are shown herein.

In this chart, machine learning applications, evolutionary computation, and knowledge processing in the biotechnology space corresponded with higher filing rates than patent application filings in general.

In this chart, Mr. Pairolero analyzed patent application filings in accordance with the country of the patent owner once the patent application granted. On the left, the patent owners from the U.S. that filed U.S. applications clearly outweighed patent owners from foreign countries. On the right, the patent owners filing applications related to AI Biotechnology patents is more dispersed.

In this chart, the allowance rate of AI biotechnology patent applications and non-AI patent applications is illustrated. As shown, there is a slightly higher allowance rate for biotechnology applications when they are not associated with AI verses the applications that are associated with AI.

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Takeaways From the U.S. Patent and Trademark Offices Artificial Intelligence and Emerging Technologies Partnership Series Part Two of Three - JD...

SINGAPORE, October 4, 2022 IDC recently released the IDC Worldwide Artificial Intelligence Spending Guide. Data shows that total global IT investment in artificial intelligence (AI) in 2021 was US$92.95 billion, expected to increase to US$301.43 billion in 2026, and the compound annual growth rate (CAGR) was about 26.5%. As for the China market, IDC predicts that China's AI investment is expected to reach US$26.69 billion in 2026, accounting for about 8.9% of global investment, ranking second in the world among other countries. In recent years, more and more enterprises have become involved in the Digintelligence Era and started the deployment of digital transformation (DX) and intelligent upgrading, which has thus spawned more demand for AI. Driven by policies, technologies, and markets, AI empowering industries is becoming a mainstream development trend.

Technology Dimension

Over the next five years, the hardware market will be the largest primary market in China's AI market, accounting for more than half of the total AI investment. IDC predicts that China's IT investment in the AI hardware market will exceed US$15 billion in 2026, close to that of the AI hardware market size of the United States. With the gradual improvement of AI infrastructure construction, hardware growth will gradually slow down, with the five-year CAGR remaining around 16.5%. The server market, as the main part of the hardware market, will account for over 80% over the five-year forecast period.

At the same time, the services market will expand at a faster pace, with the five-year CAGR expected to be about 29.6%. Total investment in the services market is expected to exceed US$4 billion in 2026, nearly four times the investment in 2021, with significant market growth. The AI services market as defined by IDC is mainly dominated by the IT services segment. IDC predicts that IT services will lead the services market growth at a five-year CAGR of 31.0%.

From the perspective of AI software, under the joint promotion of the gradual development of technologies including machine learning (ML) and computer vision, China's policy environment, and the gradually diversified customer needs, China's AI software market share will increase year by year, and more than 25% of the AI marketrelated IT investment will flow to software in 2026. In terms of growth rate, the AI software market will become the fastest-growing primary market during the five-year forecast period, with a five-year CAGR of about 30.4%. From the perspective of the technology segment, AI platforms will absorb more than 70% of software-related spending over the next five years and will become an important driving force for software market growth at a five-year CAGR of 33.1%.

Industry Application

IDC predicts that the AI-related spending of users in the four major endpoint industries professional services, government, finance, and telecom will continue to lead over the five-year forecast period, which will collectively exceed 60% of the total spending of China's AI market. Specifically, local governments. AI spending will lead AI investment growth with a five-year CAGR of 24.3% and is expected to exceed US$2.51 billion in 2026; and it is expected that the central government will have a five-year CAGR of 19.4% and reach US$1.37 billion in 2026. The market size of the financial sector represented by banks will continue to grow over the next few years, with the five-year CAGR expected to exceed 21.0%. In addition, the construction, discrete manufacturing, and healthcare industries have also achieved high growth rates, jointly promoting the development and application of China's AI. In the future, AI will be applied in various industries, with expansion in both width and depth. It will further effectively support the industries to achieve intelligent marketing and decision-making. At the same time, the deep integration of AI with industries will stimulate more potential and foster more business opportunities.

Use Case

Based on research conducted on the industries mentioned earlier, IDC Worldwide Artificial Intelligence Spending Guide covers 29 typical key AI use cases, which will be updated to reflect the market dynamics. Three use cases, namely, augmented customer service agents, public safety and emergency response, and smart business innovation and automation, will continue to remain dominant. The three together will account for more than 30% during the five-year forecast period. Currently, there are mature applications of AI in various industry use cases. It completes independent judgment and behavior learning through technologies, such as deep learning, computer vision, and image recognition, to solve a variety of complex tasks, laying an important foundation for the intelligent transformation of the industry. In use cases, customer service agents are developing rapidly, with cases in finance, retail, and other industries. Public safety and emergency response are mainly involved in the field of government security, and biometrics is used for fingerprint and face recognition. In the future, with the increasing development of AI chips, 5G, and other technologies, AI will also be better implemented in more fields.

-Ends-

About IDC Spending Guides

IDC's Spending Guides provide a granular view of key technology markets from a regional, vertical industry, use case, buyer, and technology perspective. The spending guides are delivered via pivot table format or custom query tool, allowing the user to easily extract meaningful information about each market by viewing data trends and relationships.

For more information about IDC's Spending Guides, please contact Vinay Gupta at vgupta@idc.com or Xueqing Zhang at xuqzhang@idc.com

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About IDC

International Data Corporation (IDC) is the premier global provider of market intelligence, advisory services, and events for the information technology, telecommunications, and consumer technology markets. With more than 1,300 analysts worldwide, IDC offers global, regional, and local expertise on technology and industry opportunities and trends in over 110 countries. IDC's analysis and insight helps IT professionals, business executives, and the investment community to make fact-based technology decisions and to achieve their key business objectives. Founded in 1964, IDC is a wholly-owned subsidiary of International Data Group (IDG), the world's leading tech media, data and marketing services company. To learn more about IDC, please visit http://www.idc.com. Follow IDC on Twitter at @IDCAP and LinkedIn. Subscribe to the IDC Blog for industry news and insights.

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China's Artificial Intelligence Market Will Exceed US$26.7 Billion by 2026, according to IDC - IDC

CHICAGO, Oct. 5, 2022 /PRNewswire/ --Artificial Intelligence (AI) in Medical Diagnostics Marketis projected to grow from USD 1.0 billion in 2022 to USD 5.5 billion by 2027, at a CAGR of 39.9% from 2022 to 2027, according to a new report by MarketsandMarkets.The application of AI in medical diagnostics is growing at a fast pace owing to factors such as growing government initiatives to drive the adoption of AI-based technologies, rise in adoption of AI solutions by radiologists to reduce work load, the influx of big data, availability of funding for AI-based startups, and the growing number of cross-industry partnerships & collaborations.

However, the lack of a skilled AI workforce, ambiguity in regulations, and the reluctance among medical practitioners to adopt these solutions are factors expected to restrain the market growth.

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"Software segment is expected to grow at the highest rate during the forecast period."

The AI in medical diagnostics market is segmented based on components: software and services. The services segment dominated the market in 2021, while the software segment is estimated to grow at a higher CAGR during the forecast period. Software solutions help healthcare providers gain a competitive edge despite the challenges of being short-staffed and facing increasing imaging scan volumes.

"The Nurse Call system segment is estimated to account for the largest share of the global market in 2022"

The application market in AI in the medical diagnostics market is segmented into in vivo and in vitro diagnostics. The in vivo diagnostics segment commanded the largest share of 96.8% of this market in 2021. The large share of this segment can be attributed to the growing adoption of AI solutions by practitioners, as these solutions help reduce human errors and improve treatment efficacy.

"The Hospital segment is estimated to account for the largest share of the AI in medical diagnostics market."

Based on end users, the AI in medical diagnostics market is segmented into hospitals, diagnostic imaging centers, diagnostic laboratories, and other end users. The hospitals segment commanded the largest share of 64.1% of this market in 2021. The large share of hospital segment can be attributed to the rising number of diagnostic imaging procedures suggested as treatment options in hospitals, the inclination of hospitals toward the digitization of radiology patient workflow and automation of treatment procedures, rise in use of minimally invasive procedures in hospitals to focus on quality of patient care, and the rising adoption of advanced imaging modalities to improve workflow efficiency.

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"North America to dominate the AI in medical diagnostics market in 2022"

The AI in medical diagnostics market has been segmented into four main regional segments: North America, Europe, the Asia Pacific, and the Rest of the World. In 2021, North America accounted for the largest market share of this market.

However, the Asia Pacific market is projected to register the highest CAGR during the forecast period. The high growth rate of the Asia Pacific market can primarily be attributed to the growth strategies companies adopt in emerging markets, improved medical diagnostics infrastructure, rising geriatric population, increasing prevalence of cancer, and the implementation of favorable government initiatives.

Prominent players in this artificial intelligence in medical diagnostics market are Microsoft (US), NVIDIA (US), IBM (US), Intel Corporation (US), Google, Inc.(Subsidiary of Alphabet, Inc) (US), Siemens Healthineers (Germany), GE Healthcare (US), Digital Diagnostics, Inc (US), Xilinx (US), InformAI LLC (US), HeartFlow, Inc (US), Enlitic, Inc (US), Day Zero Diagnostics, Inc(US), Aidence (Netherlands), Butterfly Network, Inc. (US), Prognos Health (US), Nanox AI (Israel), Viz.ai, Inc (US), Quibin (Spain), Qure.ai (India), Therapixel (France), Aidoc (Israel), Koninklijke Philips N.V. (Netherlands), Lunit. Inc (South Korea), EchoNous Inc. (US).

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Artificial Intelligence (AI) in Medical Diagnostics Market worth $5.5 billion by 2027 - Exclusive Report by MarketsandMarkets - PR Newswire