Industrial Engineering Archives - SwissCognitive | AI Ventures, Advisory & Research

Researchers Identify 6 Challenges Humans Face with Artificial Intelligence

SwissCognitive's Featured Article — Sat, 01 Apr 2023 03:44:00 +0000

A UCF professor led a study that identifies 6 challenges humans must overcome to enhance our relationship with artificial intelligence and to ensure its use is ethical and fair.

University of Central Florida professor and 26 other researchers have published a study identifying the challenges humans must overcome to ensure that artificial intelligence is reliable, safe, trustworthy and compatible with human values.

The study, “Six Human-Centered Artificial Intelligence Grand Challenges,” was published in the International Journal of Human-Computer Interaction.

Ozlem Garibay ’01MS ’08PhD, an assistant professor in UCF’s Department of Industrial Engineering and Management Systems, was the lead researcher for the study. She says that the technology has become more prominent in many aspects of our lives, but it also has brought about many challenges that must be studied.

For instance, the coming widespread integration of artificial intelligence could significantly impact human life in ways that are not yet fully understood, says Garibay, who works on AI applications in material and drug design and discovery, and how AI impacts social systems.

The six challenges Garibay and the team of researchers identified are:

Challenge 1, Human Well-Being: AI should be able to discover the implementation opportunities for it to benefit humans’ well-being. It should also be considerate to support the user’s well-being when interacting with AI.

Challenge 2, Responsible: Responsible AI refers to the concept of prioritizing human and societal well-being across the AI lifecycle. This ensures that the potential benefits of AI are leveraged in a manner that aligns with human values and priorities, while also mitigating the risk of unintended consequences or ethical breaches.

Challenge 3, Privacy: The collection, use and dissemination of data in AI systems should be carefully considered to ensure protection of individuals’ privacy and prevent the harmful use against individuals or groups.

Challenge 4, Design: Human-centered design principles for AI systems should use a framework that can inform practitioners. This framework would distinguish between AI with extremely low risk, AI with no special measures needed, AI with extremely high risks, and AI that should not be allowed.

Challenge 5, Governance and Oversight: A governance framework that considers the entire AI lifecycle from conception to development to deployment is needed.

Challenge 6, Human-AI interaction: To foster an ethical and equitable relationship between humans and AI systems, it is imperative that interactions be predicated upon the fundamental principle of respecting the cognitive capacities of humans. Specifically, humans must maintain complete control over and responsibility for the behavior and outcomes of AI systems.

The Intelligent Warehouse

SwissCognitive's Featured Article — Wed, 19 Oct 2022 05:44:00 +0000

Autonomous mobile robots, storage and retrieval systems, and scheduling tools get an artificial intelligence boost to improve material movement efficiencies and keep workers safe.

Manufacturing technology advances have extended the realm of automation beyond plant floor sensors, controllers, vision systems, and robotics into closely connected data collection and analysis using artificial intelligence. These technologies enable a “smart factory” to self-optimize and adapt to conditions in real-time. Despite these advances, warehouse and related material moving operations tend not to be nearly as modernized as plant floor operations.

“If you look inside the most modern environment, warehouse or factory, material handling, broadly speaking, is mostly analog,” says Matthew Rendall, CEO of Otto Motors, a maker of autonomous mobile robots (AMRs). “Any place where a forklift truck is driving something around, it is highly analog. That means the amount of data you have at your fingertips to analyze is limited. For decades, operators have been grasping at low accuracy, low frequency, and expensive-to-capture data trying to figure out how to run a continuous improvement program.”

For example, you can go into a factory or a warehouse today and still see industrial engineers sitting in lawn chairs at an intersection in the plant with a clip board, pencil, and stopwatch to monitor material flows, says Rendall. “It is an expensive thing to request of a highly trained industrial engineer, so it doesn’t get done frequently,” he says.

Which means the process, by its nature, is not as exact as it should be; plus, it’s rarely updated.

But this antiquated, analog surveying method is shifting in response to the decreasing cost of computer storage, increasing compute power, and new tools that target warehouse and distribution operations.

The roaming robot

Otto Motors is delivering tools to help industrial engineers automate the study of time and motion—and it comes in the form of the AMR. “The remarkable contrast that an AMR brings to the table is that we have sub-inch and sub-second level accuracy, and the marginal cost to collect an additional survey is zero,” Rendall explains. “So you are able to run industrial engineering time and motion studies on steroids at all times[…]

Dr. Muhannad Alomari

Andy Fitze — Thu, 16 Dec 2021 09:47:01 +0000

Muhannad completed his PhD in AI & Robotics from the School of Computing, University of Leeds. His thesis was awarded the highly prestigious EurAI Dissertation Award 2017, by the European Association for AI; and awarded the Research Excellence Award from University of Leeds. His research interests are in Natural Language Processing, Robotics, and Computer Vision. Industrial Engineering, Mechanical Engineering

Der Beitrag Dr. Muhannad Alomari erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

The AI Trajectory 2021 – Global AI Experts’ & Leaders’ Perspective

Livia Spiesz — Sun, 20 Dec 2020 00:24:23 +0000

2020 serves as a milestone in the history of digital transformation for many organisations if not industries. With some of the most remarkable AI leaders and experts on board, here is an overview of the AI trajectory in 2021.

Article by Livia Spiesz, SwissCognitive, based on AI experts’ and leaders’ input at SwissCognitive’s CognitiveVirtual, 17 Dec 2020

With the input from some of the most outstanding minds in the AI ecosystem, it is clear: the gear has been shifted – the long-standing opportunity for digital transformation supported by cognitive technologies has turned into a necessity. In fact, there may not only be a need for changing gears, but also for an entire reset. This is due to historic data losing its relevance fast and massive amount of new high-frequency data being created.

Putting the spotlight on various industries from the perspective of AI, we can recognise a sharp shift – what was not priority before, from one day to the other, became urgency. In healthcare for instance, where the need for urgent drug development landed on top of humankind’s global agenda, we can witness a more active deployment of cognitive technologies translating into more optimized and accelerated processes, directly impacting research, development and clinical trials. In virtual healthcare services supported by new technologies have also accelerated – reaching regions where access to medical assistance before was not possible. In industrial engineering, based on an extraordinary amount of data that is at a phase that humans are unable to process, the application of AI has increasingly started to contribute to more accurate forecasting, supporting the entire process of the supply chain. In cyberspace – where the use of AI is clearly twofold, being that to our best and worse advantage – both the attack and defence side increased speed of development. In the food industry, where balancing the harmony of demand and supply is an growing challenge, due to rapid fluctuations in the climate and weather, pandemics, droughts and tsunamis for instance “swarm AI” is gaining an increasing role. Finally, with the core intending to sustain our natural world, there have been also great leaps forward in the application of AI and Deep Learning.

While approaching the end of the challenging 2020, the shift in the use of cognitive technologies has not only been outstanding across industries but also across organisations. To keep up with peers and competitors, the pressure is certainly on for organisation for deploying AI, but if it is not done responsibly, there is a backslash from various stakeholders in the ecosystem. This is where ethics and responsible AI come into play and will become even more vital in 2021 and beyond putting its stamp on technology development throughout industries and organisations.

Joint forces across disciplines, academia, research, society, business, the private and the public sector, NGOs, with leaders and experts in prominent positions will continue playing a fundamental role in the forthcoming year to ensure that the development serves the benefits of all layers and aspects of humanity. Having open and transparent discussions with as many stakeholders as possible at the same table with clear intention of emerging technologies augmenting human qualities and capabilities will skyrocket to the top of the global agenda. In 2021, the now minimal yet growing carbon footprint of AI will gain priority too, as well as the solution to balanced data-sharing privacy – creating a parallel line between development and the discretion of personal data.

On a whole, our global challenges in 2021 will demand our openness and trust of cognitive technologies even more. With international collaboration we can achieve this on the common grounds of ethics, privacy, security and reliability. We will need to shift our focus from our challenges to solutions and recognise how technology can support us in various aspects of our lives. To prepare for the future, we will urgently need to start thinking about upskilling and reskilling – ensuring that humans and technology develop hand in hand. We will need to take automatization more seriously and put human qualities into action where technologies cannot reach. With the arrival of the new year, we will need to start making better use of the thousand years’ worth of data and turn it to business’ and society’s advantage.

We must not forget: Technology on its own is neutral. It is the human factor that makes a difference. By blending human and artificial intelligence we will achieve another level in wisdom.

FURTHER DETAILS:

CognitiveVirtual event recording – HERE

CognitiveVirtual event website – HERE

Der Beitrag The AI Trajectory 2021 – Global AI Experts’ & Leaders’ Perspective erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Dispatches from the front lines of human-robot collaboration

Andy Fitze — Mon, 13 Nov 2017 05:21:00 +0000

When lingerie brand Cosabella announced that it’d moved away from its digital marketing agencies in favor of artificial intelligence, companies across the board took note. The two aspects of its decision that got the most attention were revenue (how much money did AI produce?) and personnel ( how many people did AI replace? ).

SwissCognitive, AI, Artificial Intelligence, Bots, CDO, CIO, CI, Cognitive Computing, Deep Learning, IoT, Machine Learning, NLP, Robot, Virtual reality, learning

" data-image-caption="" data-medium-file="https://i0.wp.com/swisscognitive.ch/wp-content/uploads/2016/11/swisscognitive.png?fit=300%2C245&ssl=1" data-large-file="https://i0.wp.com/swisscognitive.ch/wp-content/uploads/2016/11/swisscognitive.png?fit=900%2C734&ssl=1" class="size-medium wp-image-5 alignleft" src="https://i0.wp.com/swisscognitive.ch/wp-content/uploads/2021/11/SwissCognitive_Logo_post.png?resize=300%2C107&ssl=1" alt="SwissCognitive" height="107" width="300">These two questions go hand in hand because, perhaps contrary to belief, not all companies are jumping at the opportunity to replace staff with autonomous technology. They’re eager to hear about AI’s potential to scale their productivity and revenue and work at a pace that can’t be achieved by human teams alone. But they’re often cautious when it comes to how AI will ultimately transform jobs we no longer need to new ones that we do.

Making time for interesting tasks

In the case of Cosabella, technology created as many jobs as it replaced. It freed up the company’s internal and external marketing teams from gathering, processing, and responding to data, and shifted their focus to creative and strategy. It revealed the need for high-frequency delivery of new creative assets, in order to combat consumers’ creative fatigue, and thereby created more demand for creative professionals. And it gave rise to a new form of human-robot collaboration, where technology focused on the data aspects of marketing and humans focused on the emotional and subjective aspects.

Early adaptors lead the way

This transformation is not unique to AI in marketing. Neither is the emergence of hybrid man-machine teams in the workplace and beyond. In a recent Wall Street Journal article, Ken Goldberg, professor of industrial engineering and operations at U.C. Berkeley, noted a growing trend toward multiplicity, or diverse groups of people and machines working together to solve problems. Of interest is the fact that these teams follow a somewhat gestaltist dynamic, where man and machine produce better outcomes when working together than either produces when working on its own.

Early AI adopters like Cosabella are setting this theory into action as man-machine teams emerge across industries. They’re not only giving us insight into how these teams work and what they look like; they’re also offering early feedback that is likely to shape the entire trajectory of the industry. […]

Der Beitrag Dispatches from the front lines of human-robot collaboration erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.