SwissCognitive Guest Blogger: Zachary Amos – “How AI Is Transforming Drone Delivery”

E-commerce has grown exponentially since the COVID-19 pandemic drove people out of stores and into their homes. Old habits die hard as consumers continue relying on their trusty devices to make purchases.

Although business has gone well for online retailers, meeting customer expectations with timely deliveries poses considerable challenges. Fortunately, artificial intelligence (AI) advancements have paved the way for leveraging drone delivery. However, some issues must be addressed before widespread adoption.

The Rise of Drone Delivery

The convenience of e-commerce has evolved shopping, whether for food, clothing, electronics or last-minute gifts. The consumer landscape has set its sights on drones for fast, reliable delivery to fulfill these orders.

Though still in its infancy, drone delivery has the potential to reduce distribution times, improve accuracy, and lower standard shipping costs for businesses and buyers. From 2019 to 2022, customers received over 660,000 drone delivery flights, with even more tests conducted to develop and improve this method.

However, despite drones’ efficiencies, the demand outweighs current capabilities. For instance, 10 years after announcing 30-minute drone deliveries for items under 5 pounds, Amazon only fulfilled 100 shipments using the technology in the United States.

If Amazon can overcome the regulatory and logistics hurdles, its promise of 30-minute deliveries will significantly appeal to consumers. The market value for same-day delivery was $5,795.64 million in 2021, while experts predicted the demand to rise by 12.65% by 2027. Drones can play a critical role in achieving customer satisfaction.

Drone Delivery: An AI Revolution

AI has rapidly infiltrated every industry, with more companies integrating the technology into their business models. The use of AI in drone delivery fleets is a game-changer for e-commerce and has already proven its effectiveness. Here are three ways AI-powered drones have changed how orders get distributed.

1. Improved Navigation

Conventional drones use GPS to navigate, which may be unreliable in some areas. However, AI-backed drones rely on real-time sensors and computer vision to take in their surroundings.

High-resolution cameras and light detection sense various landscapes, synthesizing the information in real time via an AI algorithm. Image and object recognition allows the drone to pinpoint potential obstacles and track different objects to avoid crashing.

LiDAR sensors also use ranging lasers to map the terrain and measure distances — drone features commonly found in the surveying sector.

2. Optimized Routes

AI can process large quantities of traffic information, such as routes, congestion, rerouting, weather conditions and buildings. Drones aren’t restricted to roadways, so the algorithm can plan the appropriate path for optimized delivery of goods with reduced flight duration.

The drone delivery fleet is also more eco-friendly than traditional methods, producing nearly 47 times fewer greenhouse gases and less energy.

3. Enhanced Security

AI’s ability to detect and avoid obstacles protects the drone and its packages. The system can also circumvent unauthorized areas during flight. As a result, in-flight safety is guaranteed and orders are delivered securely.

Companies can also use AI to detect maintenance issues with drones, allowing them to ensure their fleet is up to date and prevent malfunctions.

AI Drones in the Future

Although AI-powered drones are a feat in technological advancements, the method isn’t perfect — a reason widespread adoption has been slow. For instance, AI may have a 74-day shorter breach time frame, saving companies around $3 million more than those who don’t use it. However, drone privacy is still a hotly debated topic.

The Federal Aviation Administration (FAA) requires drones to publicize their locations, meaning anyone can view the destinations and track their flight routes. A privacy breach could lead to unsolicited advertisements and the release of personal information.

Drones are also vulnerable to state and city regulations. Phoenix would be an ideal location to set up drone delivery. However, much of the city has restricted airspace, with several small airports and an Air Force base. The FAA would hesitate to allow drones to fly without a pilot for safety reasons.

Additionally, although rural areas could greatly benefit from drone delivery fleets, densely populated suburban and metropolitan regions might pose more of a problem. AI-powered drones can navigate obstacles, but trees, swimming pools, animals, cars and people may challenge the most advanced software. Inclement weather may also be an issue, as not every area has dry, sunny weather conditions year-round.

AI-Powered Drone Delivery Has Potential

AI-backed drones can improve e-commerce and ensure consumer satisfaction in package delivery. However, industries must overcome regulatory and logistics issues before the world relies on them to do the job correctly.

About the Author:

Zachary Amos is the Features Editor at ReHack, where he writes about artificial intelligence, cybersecurity and other technology-related topics.

Der Beitrag How AI Is Transforming Drone Delivery erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Most organizations see the need to revamp their training programs to address AI skills shortages — an approach that delivers intangibles hiring can’t provide.

Copyright: cio.com – “IT Leaders’ AI Talent Needs Hinge On Reskilling”

CIOs and HR managers are changing their equations on hiring and training, with a bigger focus on reskilling current employees to make good on the promise of AI technologies.

That shift is in no small part due to an AI talent market increasingly stacked against them. With AI talent in high demand, the shortage of AI technicians available will only get worse, some hiring experts say, as job postings for workers with AI expertise are growing 3.5 times faster than for all jobs, according to a recent PwC report.

Worse, university pipelines don’t appear to be providing relief anytime soon. Although some colleges already offer AI classes, many haven’t had time to create new programs to meet the increased demand from the new AI boom, which started with the launch of ChatGPT in November 2022.

“We’re going to have much more demand than we have supply, at least until people start to skill up, and at least until universities start to have graduates who come out with expertise,” says Malavika Sagar, senior vice president and CHRO at TE Connectivity, a manufacturer of sensors and parts used in appliances, wearable devices, intelligent buildings, vehicles, and military aircraft. “I do believe we’re going to have a little bit of a crunch here for the next four to five years.”

As a result, organizations such as TE Connectivity are launching internal training programs to reskill IT and other employees about AI. Such programs, IT and HR leaders believe, will give their organizations added benefits that a hiring-heavy approach to AI needs isn’t likely to provide.

Rethinking talent strategies

To address its AI crunch, TE Connectivity just launched a four-tier training program that will range from basic education about AI and how to use it in office jobs to ways engineers can use AI to help design specific products. The company is also working with universities on AI-based product design challenges for students.[…]

Read more: www.cio.com

Der Beitrag IT Leaders’ AI Talent Needs Hinge On Reskilling erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Zachary Amos – “Will AI Reduce or Deepen the Digital Divide?”

The aviation sector is increasingly relying on internet-connected devices and interconnected digital systems, increasing the risk of cyberattacks and data breaches. Needless to say, an urgent solution is essential, which could be artificial intelligence (AI). AI is fast, versatile and adaptive, so it seems like the ideal tool. Can it permanently improve aviation cybersecurity?

Cybersecurity Concerns in the Aviation Industry

A ransomware attack is one of the most common cyberattacks. Globally, the aviation sector experiences one at least once per week on average. Threat actors know airlines are desperate to keep planes in the air, so they’re easy target.

Data breaches are another huge cybersecurity concern in aviation. Hackers know commercial airlines store passenger data — names, addresses and birth dates — which can be very valuable on the dark web. They covertly infiltrate systems to steal and leak information.

An insider threat is a threat that comes from inside an organization. Unfortunately, it’s relatively common. In 2023, 30% of chief information security officers felt it was one of the biggest dangers. More often than not, an employee’s mistake causes a cybersecurity incident.

Why Do Cybercriminals Target Aviation?

Threat actors and cybercriminals will target any sector as long as they can steal valuable data and secure ransom payments. However, aviation is a prominent target for these reasons.

Military Aviation

While the Air Force is advanced enough to stop many cyberattacks relatively easily, it deals with a tremendous number of them. Foreign countries, terror groups and digital attackers know the sensitive, top-secret data in military systems would be incredibly valuable on the dark web.

Commercial Aviation

Threat actors frequently concentrate their efforts on civil aviation. In 2020, 61% of aviation-related cyberattacks targeted commercial airlines. They’re valuable targets since they store a massive amount of personally identifiable data and get government funding.

Cyberattacks can force airlines to ground their planes until they resolve the situation, causing billions of dollars in lost revenue and refunds. Compliance-related fees and reputation damage can also have a massive financial impact on them.

Artificial Intelligence Alleviates Cybersecurity Concerns

AI can alleviate aviation’s main cybersecurity concerns and adapt to emerging threats.

• Updates and Patches

If software doesn’t receive updates, new vulnerabilities appear — and hackers exploit them. In response, aviation engineers use generative AI to expedite code base testing to guarantee patch installation, securing critical systems against cyberattacks faster.

• Incident Response

While cybersecurity professionals don’t work around the clock, cybercriminals do. Fortunately, AI can automatically respond to cybersecurity incidents during off-hours. It can either send critical alerts to prompt manual intervention or initiate a predetermined reaction.

• Autonomous Adaptation

Machine learning models can autonomously adapt as they receive new information. In other words, they don’t need any manual intervention to learn. They’ll get more accurate over time instead of becoming outdated like most other hardware. As a result, they know how to react to unique cybersecurity threats.

• Threat Detection

AI can learn from past cybersecurity incidents to understand what suspicious activity and anomalies look like. It enables predictive analytics, which essentially allows airlines to predict when and how cyberattacks will occur.

• Automatic Operation

Algorithms operate automatically, enabling them to work incredibly fast. According to Matthew Strohmeyer — an Air Force colonel — one of the military’s machine learning models can complete a task in 10 minutes that would take humans days. AI can work around the clock without manual intervention to detect, categorize and respond to threats.

Artificial Intelligence Strengthens Cybersecurity Efforts

Algorithms check all the boxes regarding cybersecurity. They’re fast, automatic, adaptable and affordable. Even commercial airlines — which have notoriously thin operating margins — can afford to leverage AI because it doesn’t need much power to run constantly.

As cyberattacks continue increasing in frequency, AI will likely become the go-to solution for aviation cybersecurity. It can protect against the most prominent concerns — ransomware, insider threats and data breaches — by minimizing human error, identifying threats faster than humans and securing systems against threat actors.

About the Author:

Zachary Amos is the Features Editor at ReHack, where he writes about artificial intelligence, cybersecurity and other technology-related topics.

Der Beitrag How Artificial Intelligence Improves Aviation Cybersecurity erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

The latest news in the world of AI: Anthropic’s substantial investment, Davos’ vital discussions on the metaverse and AI ethics,  and AI’s transformative influence in corporate giants.

Read about AI’s role in narrowing the digital gap and Google DeepMind’s exceptional problem-solving prowess.

Witness the fusion of human ingenuity with AI precision and examine healthcare’s AI evolution inspired by aviation’s strict safety standards.

Enjoy, and #ShareForSuccess!

Best regards,

The Team of SwissCognitive

Der Beitrag Leaders on AI erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Bloggers: Bidyut Sarkar, Senior Solution Manager, IBM USA and Rudrendu Kumar Paul – Boston University, Boston, USA – “Conversational AI on Manufacturing Floors With NLP-Enabled Assistants”

Takeaways:

• NLP-enabled assistants are transforming manufacturing by simplifying human-machine interactions.
• Industry giants like Toyota, Boeing, and Shell have witnessed enhanced efficiency and reduced errors through AI integration.
• The future of manufacturing envisions plant floors driven by data-rich, conversational interactions.

The advent of artificial intelligence in the manufacturing sector has brought a transformative era. As industries evolve, the integration of AI technologies becomes not just advantageous but essential. Natural language interfaces are a pivotal innovation among the myriad of advancements. These interfaces, rooted in human language and cognition principles, offer a seamless bridge between intricate machine operations and human understanding. In contemporary production settings, the ability to communicate with machines using everyday language can redefine operational efficiency. Such interfaces eliminate the barriers of complex coding languages, making data queries and command executions more intuitive. The shift towards these natural language interfaces underscores a broader movement in manufacturing: embracing AI not as a mere tool but as a collaborative partner. This partnership, built on the foundation of mutual understanding, promises to reshape the dynamics of production floors, making them more agile, responsive, and intelligent.

For Europe, the anticipated compound annual growth rate (CAGR) for the natural language processing industry from 2023 to 2030 is projected to be 15.19%, leading to an estimated market value of $17.41 billion by the end of the period. (Statista)

Source: Statista

At the same time, the value added by the manufacturing market in Europe is anticipated to reach $3.54 trillion in 2028, with an expected compound annual growth rate (CAGR) of 3.93% from 2023 to 2028. (Statista)

Source: Statista

The Role of Conversational Interfaces in Manufacturing

Conversational interfaces represent a paradigm shift in how humans interact with machines. At their core, these interfaces harness the nuances of human language, enabling a more intuitive communication pathway with technological systems. In the context of manufacturing, this innovation holds profound significance. Historically, interactions with machines required specialized knowledge, often demanding intricate command sequences or coding.

Conversational interfaces, on the other hand, simplify this interaction. They allow operators to engage with systems using natural language, making the process more accessible and less daunting. This shift democratizes access and accelerates response times as the need for translating thoughts into machine-specific commands diminishes.

Comparing conversational interfaces with their traditional counterparts reveals stark contrasts. Standard interfaces, often graphical or command-line-based, necessitate a learning curve and can limit responsiveness. Causal models break these barriers, offering a more fluid, adaptive, and user-centric approach. In essence, the evolution from traditional to conversational interfaces in manufacturing marks a transition from rigid, prescriptive systems to more flexible, understanding, and adaptive ones. This transition holds the potential to redefine the efficiency and adaptability of manufacturing processes. (Swiss Cognitive)

Technological Foundations

The underpinnings of conversational interfaces lie in two pivotal technological advancements: Natural Language Processing (NLP) and neural networks. NLP, a subfield of artificial intelligence, delves into the interaction between computers and human language. Its primary objective is to enable machines to understand, interpret, and generate human language meaningfully and contextually relevantly. This understanding forms the bedrock of any conversational interface, ensuring that interactions are syntactically correct and semantically coherent.

Neural networks, inspired by the structure and function of the human brain, play a complementary role. (IJAIM) These interconnected algorithms process information in layers, allowing for recognizing patterns and relationships in vast datasets. In NLP, neural networks facilitate the deep learning processes that drive language comprehension, sentiment analysis, and response generation.

When NLP and neural networks converge, the result is a conversational interface capable of understanding intricate language patterns, discerning context, and generating appropriate responses. Unlike traditional systems that rely on explicit programming for every possible interaction, these interfaces learn and adapt. They draw from vast linguistic datasets, refining their understanding with each interaction. This continuous learning, underpinned by the combined might of NLP and neural networks, empowers conversational interfaces to be dynamic, adaptive, and increasingly attuned to the nuances of human language. In the manufacturing sector, this translates to responsive and predictive interfaces, heralding a new age of intelligent interaction.

Leading Innovators in the Field

Several trailblazers have emerged in the dynamic landscape of conversational interfaces, each carving a distinct niche with innovative solutions. CoPilot.ai, Sigma, and Arria NLG have garnered significant attention for their pioneering contributions to manufacturing.

CoPilot.ai stands at the forefront of integrating artificial intelligence with human-centric design.

Their platform emphasizes intuitive interactions, ensuring operators can query and command production systems seamlessly. By prioritizing user experience, CoPilot.ai has managed to bridge the gap between sophisticated AI algorithms and the practical needs of manufacturing floors.

Sigma, on the other hand, has taken a data-driven approach. Their platform harnesses the power of big data analytics, combined with NLP, to offer insights and recommendations. This means real-time feedback, predictive maintenance alerts, and actionable insights that can significantly enhance operational efficiency in manufacturing. Sigma’s strength lies in transforming raw data into meaningful, actionable intelligence.

Arria NLG, focusing on the Natural Language Generation, brings a fresh perspective. Instead of merely understanding or interpreting human language, Arria NLG’s solutions excel in generating human-like text based on data. In manufacturing, this capability translates to detailed reports, summaries, and explanations generated on the fly, providing operators with a clear understanding of complex processes and data streams.

These innovators are redefining the boundaries of what’s possible in manufacturing. While varied in approach, their unique solutions share a common goal: to enhance the symbiotic relationship between humans and machines. By doing so, they are not only elevating the capabilities of individual operators but also setting the stage for a more collaborative and intelligent manufacturing future.

Source: Avnet

Real-world Applications and Case Studies

The theoretical promise of AI-powered conversational interfaces is compelling, but it’s in real-world applications where their transformative potential truly shines. Several industry giants have already begun harnessing these technologies, yielding tangible benefits.

Synonymous with automotive excellence, Toyota has integrated AI-powered assistants into its production lines. The primary objective was to combat the perennial challenge of downtime. By leveraging these advanced interfaces, Toyota’s operators can swiftly diagnose issues, receive instant feedback, and implement corrective measures. The result was a significant reduction in unproductive hours, ensuring that assembly lines run smoother and more efficiently.

Boeing, a behemoth in the aerospace sector, has turned to conversational interfaces to streamline its intricate manufacturing processes. Given the complexity of aircraft production, even minor inefficiencies can lead to substantial delays. Boeing’s adoption of these interfaces has enabled its engineers and technicians to access critical data, seek clarifications, and receive guidance without wading through cumbersome manuals or databases. The outcome has marked improved workflow efficiency and reduced production bottlenecks.

Shell, a global leader in the energy sector, faces the daunting task of managing vast and complex operations. The introduction of AI-guided processes has been a game-changer. These systems assist in monitoring equipment, predicting maintenance needs, and even guiding operators in crisis scenarios. The result is a more streamlined operation with a notable decrease in errors, leading to safer and more efficient energy production.

Beyond these industry leaders, several other enterprises have embraced the power of conversational AI. For instance, pharmaceutical companies use these interfaces for precision drug formulation, while textile manufacturers employ them for quality control. The common thread across these applications is straightforward: conversational interfaces, backed by robust AI, are ushering in a new era of enhanced productivity, reduced errors, and more intuitive human-machine collaboration.

Benefits of AI-Powered Production Assistants

Integrating AI-powered production assistants into manufacturing processes has ushered in a series of tangible benefits that are reshaping the industry landscape. One of the most pronounced advantages is the substantial reduction in downtime. By providing real-time diagnostics and predictive insights, these assistants enable swift identification and rectification of issues, ensuring that production lines remain operational and minimizing costly disruptions.

Furthermore, the precision and vigilance of AI assistants have led to a marked decrease in errors and mistakes. Unlike human operators, AI systems maintain consistent accuracy and may overlook anomalies or misinterpret data under pressure. Their ability to process vast amounts of data quickly and identify discrepancies means that potential issues are flagged and addressed before they escalate.

Lastly, the overarching impact of these advancements is the enhancement of overall efficiency and productivity. Production rates improve with streamlined workflows, instant access to data, and the elimination of common bottlenecks. Moreover, operators, freed from routine troubleshooting, can focus on more value-added tasks, driving innovation and quality.

In essence, adopting AI-powered assistants in manufacturing is not just about automating processes; it’s about elevating the entire production ecosystem to new heights of excellence.

The Future of Conversational Plant Floors

The journey through the intricacies of NLP-enabled assistants underscores their transformative potential in reshaping manufacturing dynamics. These advanced interfaces, bridging human intuition with machine precision, promise a future where communication barriers on production floors become relics of the past. As industries evolve, the vision is clear: plant floors will become hubs of data-driven conversations, where machines execute commands and offer insights, fostering a collaborative atmosphere. This synergy between human expertise and AI-driven insights is set to redefine manufacturing, heralding an era where conversational interactions drive innovation, efficiency, and unparalleled growth.

About the Authors:

Bidyut Sarkar, Fellow of the IET (UK) and author of books on AI is an expert in life sciences and industrial manufacturing industry solutions with applied AI/ML experience, having served as a keynote speaker and judge at startup competitions. His professional experience has taken him to various parts of the world, including the USA, Netherlands, Saudi Arabia, Brazil, Australia, and Switzerland.

Rudrendu Kumar Paul is an applied AI and machine learning expert and the author of multiple books on AI, with over a decade of experience in leading data science teams at Fortune 50 companies across industrial high-tech, automation, and e-commerce industries. Rudrendu holds an MBA, an MS in Data Science from Boston University (USA), and a bachelor’s degree in electrical engineering.

Der Beitrag Conversational AI on Manufacturing Floors With NLP-Enabled Assistants erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Plunge into the newest advancements, discoveries, and perspectives from the AI universe. Keep abreast and feel invigorated as we journey through the continually changing realm of artificial intelligence collectively.

Here is a peek view into this week’s Featured News:

AI in aviation
AI Industry Experts’ Insights On AI’s Creative Potential
Mining brought to a new level with AI
AI Regulation Pros and Cons
Empowering food safety with AI
AI in e-commerce
…and more.

Enjoy the read!

Kind Autumn Regards,

The Team of SwissCognitive

Der Beitrag AI is Creative and Discovers New Metals erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Alessandra Curioni, IBM Fellow, VP Europe and Africa, Director IBM Research – Zurich. “AI is getting smarter. With foundation models, proper guardrails are crucial.”

Have you ever seen the night sky over Ticino, in southern Switzerland?

Look up.

Stars as if pinned onto a black velvet, with the Milky Way stretching over the curvature of the sky. And to truly capture and understand the data about the vastness of space, artificial intelligence has been indispensable. But while in astronomy AI helps us spot new supernovas and try to uncover the mysteries of dark matter, more down to Earth AI technology deals with people. And when it comes to people, AI, just like any other emerging technology, carries with it certain risks that need to be assessed and mitigated.

After all, AI is maturing at a breakneck speed, helping humans across a multitude of industries and impacting our lives daily. At IBM Research, making sure that AI is used responsibly is of paramount importance. Policymakers and industry must ensure that as the technology matures further, it remains secure and trusted, with precise regulations. Such as those outlined in European Commission’s draft Artificial Intelligence Act, but on the global level.

Especially today, with the advent of Foundation Models and Generative AI that enable machines to generate original content based on input data, positive transformational power of AI for business and society is increasing enormously. And it is amplifying issues related to bias, reliability, explainability, data and intellectual property – issues that require a holistic and transparent approach to AI.

That’s exactly why we at IBM have just introduced WatsonX. It’s a powerful platform for companies seeking to introduce AI into their business models, with a feature for AI-generated code and a huge library of thousands of AI models. WatsonX allows users to easily train, validate, tune, and deploy machine learning models and build AI business workflows. And crucially, doing so with the right governance end to end, with responsibility, transparency and explainability. Our expectation is that the new AI tools will be integrated much easier into fields like cybersecurity, customer care and elements of IT operations and supply chain, in the most responsible way.

Unlike the previous generation of AI aimed at a specific task, foundation models are being trained on a broad set of unlabeled data. They rely on self-supervision techniques and can be used for a variety of tasks, with minimal fine-tuning. They are called foundation models because they can be the foundation for many applications of the model, applying the learnt information about one situation to another with the help of self-supervised learning and transfer learning. And they are now starting to be applied in a variety of areas, from the discovery of new materials to developing systems that can understand written and spoken language.

Take IBM’s CodeNet, our massive dataset of a lot of the most popular coding languages, including legacy ones. A foundation model based on CodeNet could automate and modernize a huge number of business processes. Beyond languages, there is also chemistry. My colleagues at the Zurich lab have recently built a tool dubbed RoboRXN that synthesizes new molecules for materials that don’t yet exist, fully autonomously. This cutting-edge technology poised to revolutionize the way we create new materials, from drugs to solar panels to better material for safer and more efficient aircraft, the list goes on. IBM has also recently partnered with Moderna to use MoLFormer models to create better mRNA medicines. And our partnership with NASA is aimed at analyzing geospatial satellite data with the help of foundation models to help fight climate change.

And soon, quantum computers will join forces with ever-smarter AI. Then, the future for countless tasks we are struggling with today will be as bright as a supernova – including material discovery. The same goes for numerous other applications of AI, from voice recognition and computer vision to replicating the complexity of the human thought process.

But to ensure that AI continues to bring the world as many benefits as possible, we mustn’t forget the importance of regulation. We need to ensure that those designing, building, deploying and using AI do so responsibly. Given the huge advantages of foundation models, we need to ensure we the economy and society are protected from its potential risks. All the risks that come with the other kinds of AI, like potential bias, apply to foundation models as well. But this new generation of AI can also amplify existing risks and pose new ones – so it’s important that policymakers assess the existing regulatory frameworks. They should carefully study emerging risks and mitigate them.

As our technology becomes ever more autonomous, it’s imperative to have moral reasoning engrained in it from the get-go. And to have guardrails ensuring that even this ‘default’ moral reasoning is unbiased, fair, neutral, ethical and explainable. We want to be able to trust AI decisions. As amazing as AI could be, with neural networks ever better mimicking the brain, we mustn’t allow it to be a black box.

To be certain that artificial intelligence and other emerging tech truly helps us make the world a better place, we have to properly regulate it now – together.

Dr. Alessandro Curioni, an IBM Fellow and Vice President of IBM Europe and Africa, is globally recognized for his contributions to high-performance computing and computational science. His innovative approaches have tackled complex challenges in sectors like healthcare and aerospace. He leads IBM’s corporate research in Europe and globally in Security and Future Computing. Twice awarded the prestigious Gordon Bell Prize, his research now focuses on AI, Big Data, and cutting-edge compute paradigms like neuromorphic and quantum computing. A graduate of Scuola Normale Superiore, Pisa, Italy, he joined IBM Research – Zurich in 1998 and leads their Cognitive Computing department.

Der Beitrag AI is getting smarter. With foundation models, proper guardrails are crucial. erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Suchita Dey – “How Are Aerospace Robotics Redefining Innovation in Aerospace Engineering?”

There has been a rise in the use of automated processes in global production with the advent of Industry 4.0. Due to its dependability, precision, and other cutting-edge characteristics, aerospace robotics in particular is one of the most widely used production solutions today. It is frequently used in sectors like the automotive industry that deal with minute electronic components.

Robots do, however, offer the same benefits to sectors like aerospace engineering that deal with larger components more frequently.

In fact, a variety of applications for robot utilization have been expanding in the aerospace industry. Together with better and more consistent quality in manufacturing, they also provide lower prices, less labor, and faster output.

Industrial automation, particularly the utilization of robots, is increasingly a must in aeronautical engineering in the modern era of digital manufacturing.

The aerospace sector has particular difficulties. The fact that aircraft are normally in operation for decades while the technology required to produce them changes quickly presents a special difficulty in terms of robotic automation. With the need for ever-increasing productivity and efficient operations, robotic automation must constantly provide a competitive edge.

Robotics Innovation in Aerospace Domain

Industrial automation, such as the utilization of robots, is now required in aeronautical engineering in the modern era of digital manufacturing. Following are some typical tasks performed by robots in aerospace engineering.

Welding

Automation has been a major trend stoking the aircraft sector over the past few years. Almost every industry is noticing it, but aerospace is where it really shines. For instance, automated welding expedites production while increasing the safety of new airplanes.

Even for experienced workers, welding poses risks. Burns, radiation, electric shock, and gases are just a few of the threats that welders may encounter that could result in serious injury. One of the most typical objectives of automation is to reduce the risk that individuals are exposed to.

Robotic welding is becoming more and more common, especially for very accurate materials like nickel alloy or titanium. Robots can accomplish this while boosting production because they can repeat the same task without compromising quality. This expedites production, strengthens the structural integrity of new aircraft, and raises worker security at assembly locations.

Drilling and Fastening

Perhaps the most common use of aerospace robotics in the aerospace sector has been the drilling and fastening procedure. For assembly line workers, this is a difficult, protracted task that needs specialized tools and a number of processes to be correctly completed. All of that can be automated, greatly accelerating the manufacturing process. Aerospace robots are able to handle the entire process, from drilling the pilot hole to reaming, even with specialist materials like titanium.

Automating this procedure is a ground-breaking development in the industry given the enormous quantity of holes that must be drilled in airplane parts. As a result of the advantageous position, it has provided many aerospace businesses, and automated drilling and fastening are now the norms in the sector.

Transportation

Large airplane components are transported from one area of an assembly facility to another, and this procedure is one area in which aerospace robotics is improving safety in the aerospace sector. Human workers may be at risk during this procedure, thus it calls for particular attention to prevent harm to other machinery or components. Because stress has been shown to increase the likelihood of human error, crane and rigging specialists may face significant levels of stress while performing their duties.

It makes sense that many aircraft firms are integrating automated transportation systems into their production lines. These hauling and rigging robots can now safely and autonomously move airplane parts while utilizing sophisticated sensors to look for people on the ground. This is a testament to how far aerospace robotics has come.

Inspection

The development of sensor technology has been very rapid lately. From smartphone cameras that can accurately detect faces to infrared sensors that can track pulse rates, modern technology is increasingly surpassing the limitations of human senses.

By using sensors to increase the accuracy of their nondestructive testing, many aerospace businesses are utilizing this technology. Aerospace robotics, however, has the potential to significantly increase both the speed and precision of inspections. They may guarantee quality and integrity by painstakingly and methodically inspecting every square inch of components for cracks and other faults.

Other structural characteristics, such as the quality of the countersink and the exit burr, can also be scanned using sensors. Robotic systems capable of automatically performing inspection scans could be configured to operate during off-peak hours, much like the sealing, painting, and coating processes. In this manner, human workers may make the most of their daily schedules to take care of any problems the inspection robots detect.

The Future of Aerospace Robotics

The potential for expansion in the field of aerospace robotics is very promising. According to Extrapolate, the aerospace robotics industry is projected to reach USD 4.9 billion by 2028, recording a robust CAGR of 11.4% during the forecast period. Engineers and physicists imagining future aircraft have recognized unmanned aerial vehicles (UAVs) as a critical component. UAVs will also be used for non-military objectives. Since the global transportation industry is under increasing pressure, more public transportation solutions are urgently needed. UAVs, according to industry leaders, could be the answer.

The Aerospace Industries Association’s “Vision 2050” initiative highlighted the potential for broad UAV adoption. Around 2050, the AIA predicts that everyone may use completely autonomous flying “pods,” comparable to aerial taxis, to move around. Modern robotic aircraft could perhaps lower carbon emissions by reducing traffic on roads and trains.

Imagine having access to affordable, widely available, and renewable energy-powered UAVs for use in public transit. In that situation, many people might substitute them for conventional automobiles or buses in the future. They would be flown using AI, much like driverless cars do now.

Meanwhile, completely autonomous assembly lines are likely to start appearing in the industrial sector of the industry soon. The above-mentioned developments allude to this development, which would aid in addressing the current lack of private aircraft as well as the rising demand for aircraft as outlined by the AIA.

About the Author:

Suchita Dey is a tech enthusiast with her recent work covering domains of technology, innovation, and delivering what influences the industry landscape! She closely follows innovation in the AI-ML landscape and writes on what’s hot in the industry.

Der Beitrag How Are Aerospace Robotics Redefining Innovation in Aerospace Engineering? erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: womblebonddickinson.com – “Greenwashing vs. Real Impact: How to Spot the Difference in AI Sustainability Claims”

Organisations using AI to support sustainability

AI has huge potential to make businesses more sustainable. Already being deployed by companies like Google to efficiently cool their data centres, in hospitality to track and reduce food waste, and by governments including Indonesia and Peru using AI and satellite data to show near-real-time vessel movements in the ocean to combat illegal and unsustainable fishing.

From a legislative stance, businesses will soon have to comply with the Corporate Sustainability Reporting Directive, which obligates financial market participants to disclose their non-financial and diversity information. Businesses are, therefore, actively looking for green solutions that can improve their marketability and ultimately their bottom line. AI is being touted as something that can manage environmental impacts and climate change while also improving business efficiency – a win-win.

Is AI sustainable?

However, when implementing AI solutions, there is often little detail given at the micro-level on how AI will save the planet any more effectively or efficiently than traditional computer-human operations. Greenwashing occurs when environmental claims are unproven, over-inflated, or just incorrect. The Advertising Standards Authority (ASA) has been cracking down on greenwashing in advertising, recently issuing reprimands to HSBC, Alpro, and Innocent, among others.

When implementing AI and measuring the energy savings it can produce, this needs to be offset against the electricity consumption of AI systems themselves, as this is potentially substantial. It has been calculated that AI’s global carbon footprint might foreseeably be equal to that of the aviation industry.[…]

Read more: www.womblebonddickinson.com

Der Beitrag Greenwashing vs. Real Impact: How to Spot the Difference in AI Sustainability Claims erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

“Redefining Business Performance with Generative AI – Virtual Conference Wrap-up”

The conference provided a thorough exploration of generative AI’s immense potential, as well as its limitations and concerns surrounding user well-being, accessibility, misinformation prevention, and job loss. From developing responsible AI to exploring captivating real-world applications, attendees discussed ways in which we could shape generative AI for generations to come. Let us now summarize the topics discussed during this engaging event while exploring how businesses are redefining their approach towards implementing new technologies such as generative artificial intelligence (AI).

Dalith Steiger, Co-Founder of SwissCognitive, World-Leading AI Network

We have heard how generative AI offers immense opportunities while posing significant challenges. One concern is user well-being, particularly for young people who may struggle to distinguish between real-life and AI-generated content.

“Generative AI: Our world, our reality” interview with Neha Shukla, Intern for Aerospace Engineering, NASA, Chair and US Representative, WEF’s Generation AI Council and Dalith Steiger, Co-Founder of SwissCognitive, World-Leading AI Network

Another is AI’s current limitations in reasoning and problems with hallucinated data. Both need to be resolved to prevent misinformation and confusion.

“How can we handle this? Unhyping the Generative AI scene in business. ” panel discussion: Jarrod Anderson, ADM, Senior Director, Artificial Intelligence – Digital & Innovation | Reema Diab, Galaxy Organization For Technology, CEO, AI expert, Futurist | Senior Consultant for Tesla, INGO’s, World Bank | Semih Kumluk, Digital Academy Senior Manager, PwC, Head of Digital Business Transformation Programs (Middle East Region) | Patrick Bangert, VP Strategic Business, Samsung SDS | Board Chair, algorithmica technologies | Deedy Das, Glean, Founding Engineer, Search & Intelligence

Another key topic was ensuring that generative AI technology is accessible. It should be available to users with minimal technological expertise, limited internet access, and low-cost devices in order to foster equal opportunities and prevent digital divides.
Developing responsible AI also includes using inclusive datasets, assembling diverse teams, implementing design justice, and moderating AI models.

“Generative AI: Flying above and beyond” use-case with Romaric Redon, Head Advisor on Artificial Intelligence Technologies, Airbus

There is understandable fear of job loss. However, this shift in the job market will not necessarily result in disappearing tasks. Rather, it will change the way we work and create new opportunities for individuals to enhance their abilities. Ultimately, this transformation in the job market can lead to a more fulfilling and satisfying work experience.

“Generative AI: What lies behind and how it propels businesses.” panel discussion: Jacques Ludik, Founder & CEO, Cortex Logic & Cortex Group, Founder & President, Machine Intelligence Institute of Africa | Noelle Silver Russel, Global AI Solutions Lead, Generative AI and LLM Industry Lead, Accenture, Chief AI Strategist, AI Leadership Institute | Andreas Welsch, Chief AI Strategist, Intelligence Briefing, VP & Head of Marketing & Solution Management, AI, SAP | Jair Riberio, Analytics & Insights Leader, Volvo Group, EMEA AI Strategist, Kimberly-Clark / Program Manager, IBM Watson | Uri Eliabayev, Founder, Machine & Deep Learning Israel, AI Consultant

We’ve also come across fascinating applications of generative AI, such as Airbus employing AI for enhancing satellite image resolution and detecting anomalies. And Signapse, which use generative AI to support deaf people by creating live, photo-realistic sign language.

“Generative AI: Opening new worlds” use-case with Ben Saunders, Generative AI Founder | Co-Founder & CTO, Signapse

Eventually, we delved into the current challenges and solutions in various domains, the importance of leadership collaboration, and the responsible use of generative AI, while also exploring how the collaboration between BCG and OpenAI could foster the business world.

“So where is the catch? Challenges and Solutions.” panel discussion: Andrea Iorio, Podcast Host, NVIDIA, Founder, Keynote Speaker & Podcaster, AIK, Andrea Iorio Keynotes | Daniela Rittmeier, Head of Data & AI Center of Excellence, Capgemini | Ori Goshen, Co-Founder and Co-CEO AI21 Labs | Dimitris Kalogeropoulos, Chief Executive, Global Health & Digital Innovation Foundation, Advisory Committee Member, Telehealth Solutions Virtual Pitch Competition, IEEE SA | Sean McClain, Founder and CEO, Absci

Overall we have seen that by embracing responsible development and accessibility of generative AI technology, we will be able to harness its immense opportunities and address its challenges.

If you missed our AI Trajectory virtual conference, here you can find the video recording:

Der Beitrag Redefining Business Performance with Generative AI – Virtual Conference Wrap-up erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.