SwissCognitive Guest Blogger: Zachary Amos – “How Countries Are Using AI to Predict Crime”

Industry 4.0 and 5.0 is using robotics to bring supply chains into the future. The last decade has been fraught with challenges, including delays, worker shortages and market volatility. Mitigating costs and enhancing the workforce are the goals of swarm robotics, and artificial intelligence (AI) is making them even more competent. See how these workers make supply chains resilient and competitive.

What Are Swarm Robotics?

Swarm robotics is a field focusing on large quantities of simple yet practical robots. These robots work best in groups to achieve straightforward tasks, making them optimal for reducing labor burdens. They also shine in industries like supply chains, where repetitive tasks take up a major portion of the working day.

Supply chains need to use swarm robotics because they are easy to manage simultaneously. They are autonomous, respond to environmental stimuli and are easy to reprogram to new tasks. The collective efforts of these machines can make decisions on the fly, covering ground from last-mile delivery to utilizing resources in a smarter way.

How Do Supply Chains Use Swarm Robotics?

These robots enhance operations while allowing supply chains to overcome common pain points. Each application for swarm robots is also made better by AI. What does this look like?

Dynamic Operations

Because swarm robots take tedious tasks away from workers, they allow people to focus on more high-level processes. In the meantime, the bots can tally inventory, navigating complex warehouses in large numbers. They are immediately deployable to do automatic updates, sending instant notifications to procurement, fulfillment and distribution teams.

Swarm robots are also ideal in changing, unstructured environments. With AI and sensor technology, they can map areas no matter how complicated they are. As they learn to navigate, they become more proficient when interacting with similar environments because of machine learning algorithms. This informs routing and navigation and allows perpetual scaling potential.

Cost Reduction

Delegating tasks to robots saves supply chains tons of money. Human error costs corporations between $50-$300 for every mistake. The increased accuracy is only one aspect of the financial savings. The robots save businesses time and money in talent acquisition processes, which take efforts away from fulfilling client needs.

However, the most prominent financial gain may be from warehouse savings. Refined inventory management prevents objects from taking up square footage and energy as they collect dust. Instead, there is detailed metadata on each item, their expiration date, market values and more, which swarm robots can collect with AI.

Productivity Gains

ot only do AI-powered swarm robots save money, they make everything more efficient. Preventing errors, defects and more can shorten lead times from suppliers. In one study, several industries experienced shortened fulfillment lead times by an average of 6.7 days.

They can also allow parallel task execution. While some robots pick up objects, others can transport them and even more can pack them. This yields numerous time savings across lengthy processes with multiple intermediaries.

There are also other productivity gains because swarm robots make supply chain environments safer for workers. They can constantly monitor unsafe conditions in real time, saving employees the trouble of entering dangerous circumstances. This means fewer workers experience injuries and incidents, allowing them to work with higher morale in safer conditions.

Preparing the Swarm

Much like swarms of ants group together to achieve a common goal, these types of robots optimize supply chains. Combining them with AI makes them even more powerful. As they advance, swarm robotics consistently prove they are a must-have fixture for supply chain management in the future.

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 Enables Swarm Robotics in the Supply Chain erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Building Tomorrow’s Tech: AI Investments in Full Swing – SwissCognitive AI Investment Radar

Welcome back to this week’s SwissCognitive AI Investment Radar, where we spotlight the latest in global AI investments shaping the tech landscape.

This week, we see a broad spectrum of strategic moves and financial commitments, from General Catalyst’s $8 billion leap into AI-driven enterprise transformation to Reflexivity’s $30 million for advancing AI-powered financial analysis. Across the Atlantic, the European Union pledges $1.5 billion for deep tech research, aiming to keep Europe competitive in an arena largely dominated by the U.S. and China.

Automotive innovation takes center stage as Toyota and NTT invest $3.3 billion to push forward predictive, accident-avoiding AI for self-driving cars by 2028. Meanwhile, Google’s $5.8 million initiative in Sub-Saharan Africa seeks to bridge the AI skills gap, enabling local talent to address challenges in health, climate, and more. AI’s influence is also reshaping sectors like architecture and construction, as industry players ramp up investments to integrate AI capabilities into their workflows.

From the defense sector, Helsing’s £350 million commitment underscores the importance of AI in national security, while Saudi Aramco’s VC arm dedicates $100 million to nurture AI startups. And as AI startups now command a third of U.S. venture funding, countries like Indonesia are positioning themselves as new hubs for AI with favorable policies and low energy costs.

Join us as we delve into these developments, capturing how AI’s financial momentum is redefining industries and shaping the future.

Previous SwissCognitive AI Radar: Tech Giants Lead AI Infrastructure, Startups Follow Suit.

Our article does not offer financial advice and should not be considered a recommendation to engage in any securities or products. Investments carry the risk of decreasing in value, and investors may potentially lose a portion or all of their investment. Past performance should not be relied upon as an indicator of future results.

Der Beitrag Building Tomorrow’s Tech: AI Investments in Full Swing – SwissCognitive AI Investment Radar erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Vishal Kumar Sharma – “Analysing the Importance of Artificial Intelligence and Robotics in Agriculture”

Introduction

As the foundation of human civilization, agriculture is undergoing a revolution right now. The agriculture sector faces hitherto unheard-of challenges given the rising world population and growing effects of climate change. Though throughout has proven successful, conventional agricultural methods are failing to meet the demands of modern society. Two innovative technologies poised to transform our farming, monitoring, and collecting of crops are artificial intelligence (AI) and robotics. The article investigates the reasons behind the necessity of artificial intelligence (AI) and robotics into agriculture rather than just a passing trend.

The challenge is supplying food for a population growing

By year 2050, the world’s population is expected to reach 10 billion. Feeding this many people calls for a 70% increase in food output, claims the Food and Agriculture Organization (FAO). Still, the resources needed for agriculture land, water, labor are few and in many cases declining. Conventional methods usually damage the environment and demand a lot of human effort. Thus, it is quite necessary to improve farming’s efficiency, sustainability, and output.

The Significance of Artificial Intelligence (AI) in Agriculture

In agriculture, artificial intelligence is the use of data-based, more intelligent decisions making. Large amounts of data in real-time analysis made possible by AI-driven systems gives farmers insightful information that may be utilized to monitor soil condition and project crop harvests. Using satellite images and weather data, artificial intelligence systems can predict ideal planting times, spot disease outbreaks, and suggest effective pest control tactics. Such a great degree of accuracy can lead to notable increases in waste reduction, crop output, and the limitation of the usage of harmful pesticides.

Furthermore, artificial intelligence powered instruments have the capacity to improve resource use efficiency. Precision agriculture driven by artificial intelligence helps farmers to precisely apply pesticides, fertilizers, and water in ideal amounts and targeted areas. This method solves the entwined problems of sustainability and financial viability by lowering costs and mitigating the negative effects of agriculture.

The Significance of Robotics in Agriculture

By automating tasks requiring a lot of manual work, robotics improves artificial intelligence and hence increases farming’s productivity and scalability. Robots are used gradually for harvesting, weeding, and planting jobs. While robotic harvesters can pick fruits and vegetables with no damage, a task difficultly accomplished with human workers, autonomous tractors can plow fields with perfect accuracy. In fields without personnel or where agricultural chores demand great physical effort, this technique is very important.

Precision farming depends much on robotic tools. With sensors and cameras, unmanned aerial vehicles can monitor crop conditions from above and provide current data that lets farmers make wise decisions. Terrestrial robots can do complex tasks including weed removal, therefore reducing the need for herbicides. These technologies not only increase output but also reduce the boring character of manual farming, so appealing agriculture is to younger generations.

Sustainability and environmental impact

Using robotics and artificial intelligence in agriculture has a clear advantage since it helps farming methods to be more sustainable. Often requiring resources, traditional agricultural techniques can lead to soil degradation, water shortage, and a decline in biodiversity. Artificial intelligence (AI) driven analytics can give farmers direction on using sustainable practices such crop rotation, minimum soil disturbance, and irrigation optimization. By enabling precise farming techniques that cut waste and environmental effect, robotics can help to forward this goal.

Artificial intelligence might, for instance, look at soil moisture data and project irrigation needs, therefore ensuring the effective use of water. By selectively distributing fertilizers and pesticides, robots can help to lower the overall consumption and thereby minimize the flow into nearby ecosystems. By maintaining soil health and biodiversity, these technologies not only protect the surroundings but also raise agricultural output.

Advantages in the field of economics

In the context of agriculture, artificial intelligence (AI) and robotics provide clear financial benefits. For farmers, these technologies could help to lower costs, increase crop output, and raise the quality of agricultural goods. By means of predictive capabilities of artificial intelligence, farmers may efficiently reduce risks related to market volatility, pests, and weather conditions, so promoting more stable income. By automating chores requiring a lot of physical labor, robotics can significantly cut labor costs. In places where agricultural labor is either scarce or highly expensive, this is particularly helpful.

Moreover, the information generated by robotics and artificial intelligence can provide farmers with other revenue streams. For example, precise information on crop quality could be used to negotiate better prices or enter special markets. Furthermore, the application of these technologies can improve farming output, therefore raising its competitiveness and maintaining the livelihoods of farmers in both developed and underdeveloped countries.

Challenges and the road forward

Though robots and artificial intelligence (AI) have great potential in agriculture, several factors prevent their general application. Mostly because of high startup costs, lack of technology knowledge, and concerns about data privacy, smallholder farmers in underdeveloped areas have great difficulties. Governments, research labs, and businesses must cooperate to provide training, subsidies, and support systems that make this technology available to all farmers thereby overcoming these challenges.

Moreover, the development of robotics and artificial intelligence in agriculture has to be guided by ideas of durability and fairness. It is imperative to ensure that these technologies benefit smallholder farmers, the basis of world food supply, as well as big-scale commercial farms as they develop.

Conclusion

Rather than only a technical development, artificial intelligence and robots are essential tools for the direction of agriculture. These technologies offer a way to reach a more efficient, ecologically friendly, and flexible agricultural system within the worldwide fight to solve the problems of feeding an increasing population and preserving the environment. Including robotics and artificial intelligence (AI) into agricultural practices has moved from a luxury to a necessary need. These technologies will help us to ensure that agriculture meets the needs of the present generation without endangering the capacity of next generations to support themselves.

References:

1. Food and Agriculture Organization (FAO). (2017). The future of food and agriculture: Trends and challenges.
2. Aravind, K. R., Raja, P., & McKee, G. (2017). A review of agriculture robotics: Current trends and future directions. Computers and Electronics in Agriculture, 142, 379-394. doi:10.1016/j.compag.2017.09.030
3. Shamshiri, R. R., Kalantari, F., Ting, K. C., et al. (2018). Advances in greenhouse automation and controlled environment agriculture: A transition to plant factories and urban agriculture. International Journal of Agricultural and Biological Engineering, 11(1), 1-22. doi:10.25165/j.ijabe.20181101.3790
4. Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming – A review. Agricultural Systems, 153, 69-80. doi:10.1016/j.agsy.2017.01.023
5. Balafoutis, A., Fountas, S., Cavalaris, C., et al. (2017). Precision Agriculture Technologies Positively Contributing to GHG Emissions Mitigation, Farm Productivity and Economics. Sustainability, 9(8), 1339. doi:10.3390/su9081339
6. Zhang, C., & Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: A review. Precision Agriculture, 13(6), 693-712. doi:10.1007/s11119-012-9274-5

About the Author:

Vishal Kumar Sharma, Senior Project Engineer of AI Research Centre, Woxsen University, India, with over 8 years of experience in team management, PCB design, programming, robotics manufacturing, and project management. He has contributed to multiple patents and is passionate about merging smart work with hard work to drive innovation in AI and robotics.

Der Beitrag Analysing the Importance of Artificial Intelligence (AI) and Robotics in Agriculture erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: webuildvalue.com – “The application of Artificial Intelligence (AI) in Webuild’s construction sites, a new frontier of innovation”

From Paris to Melbourne, here are the worksites where AI to serve workers is being tested.

The Only Limit is Imagination. The application of artificial intelligence to the construction of large-scale projects, and specifically to construction site activities, are still largely unexplored. However, ongoing innovations in experimentation promise to revolutionize the industry. The use of robotics, the connection and integration of machines, sensors, and devices on site that “communicate” with each other, predictive maintenance driven by data collection and analysis, and augmented reality for staff training are just some examples of what artificial intelligence can do and how it can help transform the construction world. Starting with safety—one of the strategic sectors where innovations can enhance productivity while also saving lives. In this regard, Webuild’s construction sites around the world represent a new frontier of artificial intelligence, where innovative solutions are tested to minimize workers’ exposure to risk.

«Globally, explains Dr. Giampiero Astuti, Head of Innovation Program Management at Webuild, there is a hierarchy of safety controls ranging from the cultural factors, so anything that is linked to human behaviors, to the technical-organizational controls, including the technological one, which aim to intervene to the source of hazard. This is where artificial intelligence comes into play.»

The application of artificial intelligence in Webuild’s construction sites is being tested precisely where the risks are highest: in the movements of large construction machinery, in workers’ behavior inside tunnels, and in the operations of TBMs, the massive tunnel boring machines that dig tunnels.

«The potential is immense, continues Astuti, and we have begun testing these new AI applications in some sites and on certain models, starting from Paris and extending to Australia».

The application of AI in the Grand Paris Express Construction Sites

One of the first applications of artificial intelligence was tested in the construction of the Grand Paris Express, the new metro network in Paris that will connect the towns of Île-de-France.[…]

Read more: www.webuildvalue.com

Der Beitrag The Application Of Artificial Intelligence (AI) In Construction Sites, A New Frontier Of Innovation erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Artem Pochechuev, Head of Data and AI at Sigli – “Future Of AI-Powered Solutions For Disabilities: On The Verge Of Fantasy”

Today, while discussing Artificial Intelligence, we often talk about Generative AI tools, virtual assistants, or recommendations assistance. Such tools are already widely adopted and that’s why it is not surprising that they come to our mind in the first turn.

However, the potential of AI is much higher than that. This technology can be used in some mind-blowing solutions that seem to be absolutely fantastic.

Nevertheless, their introduction can be much closer than we may think.

In this article, we offer you to take a look at the most cutting-edge AI-powered projects that can greatly change the lives of people with disabilities (and not only).

Multimodal LLMs

Let’s start with something that sounds the most realistic – multimodal LLMs. Probably, all of you are already well-familiar with models that can work only with text inputs and provide text outputs.

Multimodal models are able to work with data in different formats. It means that they can deal with text, images, and sounds simultaneously and provide a relevant output. That is exactly what GPT-4o is expected to offer.

Of course, such models can be highly helpful for everyone. But their value will be significantly higher for people with different kinds of disabilities, including those with vision impairment, physiological disorders, and mental diseases.

Multimodal LMS can act as full-scale virtual assistants. Their functionality can offer much more possibilities in comparison to well-known solutions like Siri.

What can multimodal LLMs offer to people who can’t interact with their surroundings in a traditional way? We can say “practically everything” and from some point of view, we even won’t exaggerate.

For example, they will be able to explain everything that is written on the screen or describe what is shown in the picture. Their functionality will allow them to instantly translate and read aloud a text from the PDF file. They will help people to interact with their computers and smartphones. Based on the voice command made by users, they will open different menus, choose the necessary options, or move a pointer to the required line, while for a person with low vision or hand tremors, it can be very challenging to do this.

In the future, such models are expected to process video content as well. This will allow them to recognize films and describe their plots for users. Or they will be able to understand what sports game you will show to them and explain the rules.

Of course, these are just a couple of examples that demonstrate how multimodal LLMs can be used by people with disabilities. The range of their applications can be really wide.

AI-powered prosthetics

For people who were born without some parts of their bodies or who lost them under different circumstances, prostheses can become the best solution. These artificial body parts can restore some of the function and appearance of the lost anatomy. However, everything is not as seamless as we may think. The use of traditional prostheses can be associated with huge discomfort and various limitations, like limitations in dexterity or sensory feedback.

Nevertheless, such issues can be at least partially addressed by AI-powered prosthetics. Yes, AI arms today are not just something from a science fiction book. That’s a reality.

Artificial intelligence can significantly enhance the functionality, adaptability, and user experience of prostheses. In such solutions, ML is applied to teach bionic limbs how to understand movement patterns and how to make predictions based on the behaviours demonstrated previously. Thanks to this, limbs become more dexterous and more “natural”.

Such prostheses, both arms and legs, are non-invasive. But they have sensors that can measure electrical signals to identify the user’s intended movement.

Photo: University of Michigan

Of course, the use of AI-powered limbs is much more convenient in comparison to traditional prostheses. AI can automatically adjust artificial limbs for a better fit and can even make real-time changes based on user movements and activity levels.

The most advanced models can provide feedback on pressure and texture, which allows them to simulate the sense of touch for users.

Nevertheless, the cost of such devices is very high at the moment. This is one of the main factors that prevent them from being widely adopted today.

Bionic eye

Bionic limbs are a cutting-edge technology but what do you think about bionic eyes?

These experimental devices can restore functional vision for people who have partial or even total blindness.

The implantation of the earliest version of the bionic eye took place in 2012. The patient who got this artificial eye suffered from profound vision loss. After the surgery, he was able to see light. However, he couldn’t make distinctions within the environment. Since then, this first eye model has been greatly improved. Some other versions helped people start seeing abstract images. Nevertheless, none of the patients has regained vision.

One of the most widely discussed projects from this category is the Prima system by Pixium Vision. Their bionic vision solutions are being developed to help patients with profound vision loss and improve their independence and mobility.

Photo: https://www.pixium-vision.com

The core of their idea is the use of a 2-by-2-millimeter square implant that should be surgically placed under the retina. This implant should receive infrared data from camera-equipped glasses and further turn it into pulses of electricity which will replace signals generated by photoreceptor rods and cones.

Some early feasibility studies conducted in the US and European Union demonstrated that this system could be potentially effective and safe for people. Nevertheless, the project faced some financial difficulties which resulted in the delay in further research and development.

Rehabilitation robots and exoskeleton

Rehabilitation is a very important process for people with disabilities and patients after injuries. AI-powered robots can greatly help in the process of physical therapy through repetitive and controlled movements. They can offer personalized exercises and continuously monitor the progress to optimize recovery outcomes.

Such robots are often used in targeted therapy for patients with neurological or musculoskeletal impairments, such as stroke, spinal cord injury, or orthopedic injuries.

One of the most well-known robots of this kind is Lokomat which helps individuals relearn walking patterns. It ensures the most physiological movement which can be guaranteed by the individually adjustable patient interface.

Photo: https://www.hocoma.com/us/solutions/lokomat

Another type of solution used in rehabilitation is the exoskeleton. Exoskeletons can be defined as wearable devices that work in conjunction with the user’s movements to enhance or assist physical capabilities.

They can help individuals with mobility impairments to stand, walk, or perform other movements. Moreover, they can be used to enhance the physical abilities of healthy individuals, such as in industrial or military applications.

Over the last several years, we could observe the growing interest in designing innovative tools of this kind that incorporate AI. The obvious benefits of such exoskeletons are their capabilities to analyze data and adjust to the individual user’s needs in real-time.

One such groundbreaking AI-powered exoskeletons was developed by a group of researchers at North Carolina State University and the University of North Carolina at Chapel Hill. This wearable device can ensure great energy savings during human movement, which could lead to great improvements in athletic performance and significantly help individuals with mobility issues.

This exoskeleton is powered by data-driven and physics-informed reinforcement learning. With this approach, wearable robots can become intuitive and predict user’s movements.  This technology can also generate synergistic assistance across different activities, such as walking or stair-climbing. The controller can automatically adapt to various kinematic patterns. It means that the transition between activities can take place without any handcrafted control.

Photo: https://www.foxnews.com/tech/ai-driven-exoskeleton-lightens-your-load-elevates-performance

Though the actual prices of exoskeletons can vary from $50,000 to $120,000, Hao Su, Ph.D., associate professor at North Carolina State University and the University of North Carolina at Chapel Hill, noted that their efficient learning-in-simulation framework allows for rapid design and testing in computer simulations.

This can help to reduce the cost of research and development.

“Looking forward, we plan to make our robots truly affordable and accessible through innovative hardware design, namely low-ratio gears and cost-effective but high-torque electric motors. In about one year, we aim to make our exoskeletons for sale at a price range of $1,500 to $4,000, depending on specific features and manufacturing scale,” he explained.

Elderly care robots/ assistive robots for people with disabilities

While talking about robots, we can’t but mention robots that could fully or at least partially replace nurses, tutors, and caregivers.

In August 2023, the first commercial general-purpose humanoid robot Apollo by Apptronik was presented to the public. At the initial stages of its development, it was planned that it would be used in the manufacturing and warehousing industries. Nevertheless, later the range of its use cases was expanded. It can be also helpful in construction, retail, and elderly care. In the latest case, such robots can handle dozens of household chores and become good companions for people who spend a lot of time in isolation due to their disease or disabilities.

Photo: https://apptronik.com/apollo

However, this project is far not the only one in this group.

For example, there are a lot of reports from Japan revealing that the country is actively investing in the automation of elder care by employing various robots.

Though probably the majority of us start thinking about humanoid devices when asked about care robots, it’s far from being true. They can come in different sizes and shapes. Some of them are intended for physical care. In this case, we are talking about those ones that can help lift patients who can’t get on their own. There are robots that assist people in exercising and moving. Some others can track the physical activity of patients, detect falls, and help them use the toilet or take a bath.

There are also robots that are intended to communicate with elderly people, they can entertain them and even conduct cognitive training.

Robot guide dogs

Guide dogs are known to have been helping people with visual impairments for centuries. They can be great assistants and companions but the use of their services is associated with a row of challenges. The training of a guide dog usually requires significant time and expense. Moreover, let’s not forget about an average dog’s lifespan. This explains why a lot of countries face a shortage of trained guide dogs.

For example, according to the data published by the China Association for the Blind, currently, there are only around 400 trained dogs in the country, while the number of people who may need their help is over 17 million.

Dogs require particular care. They all have their personalities. This also can cause some difficulties for people.

But with the application of modern technologies, such issues can be successfully solved. Especially for those who do not feel like having a furry friend, as they can have a robot friend instead.

Robot guide dogs can efficiently provide real-time navigation services for people with visual disabilities and let them travel independently and safely. Such robots can identify road conditions, obstacles, and surrounding facilities. Moreover, they can work with voice prompts and provide vibration feedback, which makes interaction with them quite simple.

It is known that a group of Chinese researchers have been already conducting field tests of a six-legged guide dog that relies on cameras and sensors for navigation. This robot can successfully recognize traffic light signals, while in the case of real dogs, this “feature” is not available.

Photo: https://edition.cnn.com/2024/07/08/china/chinese-robot-guide-dog-intl-hnk/index.html

Of course, robot dogs require some maintenance but at least users do not need to feed them on a daily basis.

Brain-computer interface

Another technology that we should mention is a brain-computer interface. It can establish a direct communication pathway between the brain and an external device. It is possible thanks to its capability to decode the neural signals associated with attempted but unarticulated speech. In other words, it can translate neuronal information into commands capable of controlling external software or hardware systems.

In a very simplified way, we can explain its work as follows:

1. Collection of brain signals using electrodes or sensors;
2. Signal processing, filtering, and amplifying;
3. Extraction of relevant patterns or features within the signals;
4. Translation of these patterns into commands that can be understood by external devices.

Some BCIs are being developed for entertainment purposes. With their help, players can enjoy more immersive experiences. However, the majority of such projects have healthcare-related goals. For example, they can be used to assist in the in the recovery of motor functions.

In this context, it’s worth recollecting Neuralink. That’s definitely one of the most widely-known projects of this kind. This BCI is fully implantable. It’s invisible. And it can help users to seamlessly control their smartphones and computers. This technology can greatly help people with disabilities who are looking for ways to become more independent. Its efficiency in this aspect has been already proven in the first human trial.

Photo: https://neuralink.com/blog/prime-study-progress-update

In January 2024, Noland Arbaugh, a 30-year-old man paralyzed from the neck down, became the first patient who received the Neuralink device. Though there were some technical challenges during the trial, the general results look quite promising.

Thanks to the Neuralink device, the young man got practically full control of a computer. With the power of his mind, he can play games and browse the web at any moment. Moreover, according to Neuralink, Noland has managed to set the human record for cursor control with a brain-computer interface.

In an interview with journalists, Noland explained that the biggest advantage of using a BCI is the possibility of being independent.

“It’s just made me more independent, and that helps not only me but everyone around me. It makes me feel less helpless and like less of a burden. I love the fact that the people around me don’t have to wait for me so much. Outside of being completely healed, I believe what most quadriplegics want is independence,” he said.

Conclusion

Though today the majority of solutions mentioned in this article haven’t been widely adopted, that’s obvious that they have great potential given their incredible social value.

Moreover, we can say for sure that the real power of technologies, and AI in particular, hasn’t been even fully explored yet.

We still have a lot of things to learn and to do. But one thing is clear: today we are close to the future as never before. And we definitely shouldn’t stop in making life easier and better for everyone with the power of AI.

About the Author:

In his current position, Artem Pochechuev leads a team of talented engineers. Oversees the development and implementation of data-driven solutions for Sigli’s customers. He is passionate about using the latest technologies and techniques in data science to deliver innovative solutions that drive business value. Outside of work, Artem enjoys cooking, ice-skating, playing piano, and spending time with his family.

Der Beitrag Future Of AI-Powered Solutions For Disabilities: On The Verge Of Fantasy erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: forbes.com – “The AI-Driven Connected Enterprise” – APIs, RPA, Automation

Successful and innovative enterprises are well connected. They are notably good at preparing and harnessing external data. Artificial intelligence (AI) can enhance sources, processes and workflows, making a well-run enterprise stronger, quicker and more competitive.

Being able to access and use data from customers, suppliers and other stakeholders is a good indicator of an organization’s capacity to make the right decisions. An externally informed mindset, according to the authors of a McKinsey report on innovative companies, is less vulnerable to biases and internal politics and enables rapid course-correction of strategies, R&D priorities and other initiatives.

Applied smartly, information can improve decision-making and erode inefficiencies. The right kind of data infrastructure is what enables a company “to break down (or at least perforate) silos,” as McKinsey puts it. What you need are integrated data connections, more structured data, and a platform or fabric that can unify workflows, tasks and analytics. All can benefit from AI.

Connectors And APIs

Data integration is a complex equation. To start with, enterprises use myriad application programming interfaces (APIs), typically paired with connectors, to link with data sources. Managing these sets is a challenge. One way we do so is through crowdsourcing, enabling the reuse and adaptation of capabilities.

Many of our clients are already familiar with the task-mining capabilities of robotic process automation (RPA) and AI/machine learning (ML) algorithms. But you also can use AI to build and manage your API infrastructure.

An emerging use case for generative AI (GenAI) is developing, optimizing and protecting APIs. (See, for instance, this Google Cloud session.) These kinds of deployments can, in turn, trigger a virtuous cycle: simplifying existing stacks of APIs, which make it easier to adopt more AI. The other prerequisite to using data is making sure that it’s in good order.[…]

Read more: www.forbes.com

Der Beitrag The AI-Driven Connected Enterprise erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: news.mit.edu – “New AI Model Could Streamline Operations In A Robotic Warehouse”

Hundreds of robots zip back and forth across the floor of a colossal robotic warehouse, grabbing items and delivering them to human workers for packing and shipping. Such warehouses are increasingly becoming part of the supply chain in many industries, from e-commerce to automotive production.

However, getting 800 robots to and from their destinations efficiently while keeping them from crashing into each other is no easy task. It is such a complex problem that even the best path-finding algorithms struggle to keep up with the breakneck pace of e-commerce or manufacturing.

In a sense, these robots are like cars trying to navigate a crowded city center. So, a group of MIT researchers who use AI to mitigate traffic congestion applied ideas from that domain to tackle this problem.

They built a deep-learning model that encodes important information about the warehouse, including the robots, planned paths, tasks, and obstacles, and uses it to predict the best areas of the warehouse to decongest to improve overall efficiency.

Their technique divides the warehouse robots into groups, so these smaller groups of robots can be decongested faster with traditional algorithms used to coordinate robots. In the end, their method decongests the robots nearly four times faster than a strong random search method.

In addition to streamlining warehouse operations, this deep learning approach could be used in other complex planning tasks, like computer chip design or pipe routing in large buildings.

“We devised a new neural network architecture that is actually suitable for real-time operations at the scale and complexity of these warehouses. It can encode hundreds of robots in terms of their trajectories, origins, destinations, and relationships with other robots, and it can do this in an efficient manner that reuses computation across groups of robots,” says Cathy Wu, the Gilbert W. Winslow Career Development Assistant Professor in Civil and Environmental Engineering (CEE), and a member of a member of the Laboratory for Information and Decision Systems (LIDS) and the Institute for Data, Systems, and Society (IDSS).[…]

Read more: www.news.mit.edu

Der Beitrag New AI Model Could Streamline Operations In A Robotic Warehouse erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Nancy Kapila, Team Lead, Roots Analysis – “AI in Clinical Trials: How Artificial Intelligence is Shaping Clinical Trials”

The current wave of advanced biologics, vaccines, and therapeutic drugs is shaping the pharmaceutical industry. Clinical trials play a crucial role in the success of novel therapeutic development, consuming almost 50% of the time and capital investment during R&D processes. It is a well-known fact that the success rate of clinical trials is only 10-20% due to inappropriate study design, insufficient patient recruitment, false predictions, and improper protocol adherence. Above all these factors, accurate data predictions play a major role in the success of drug development. To address these concerns, Artificial Intelligence emerges as a most disruptive technology, enabling automation, advanced analytics, and real-time data prediction—saving plenty of time wasted on data collection and patient recruitment for drug efficacy and safety analysis. Ironically, when combined with advanced technology such as digital twins or organ-on-a-chip, AI in clinical trials streamlines clinical trial data and reduces human errors in data collection.

Importance of AI in Clinical Trial

The conventional drug development process is extremely complex and time-consuming. On average, it takes around 10 years to develop a novel drug and a capital investment of around USD 2.5 billion. Clinical trial failure delays the supply of drugs into the market and puts a financial burden on pharma companies. During clinical trials an enormous amount of data has been generated and requires detailed evaluation. Analysis and management of large amounts of data can be challenging without robust analysis tools.  However, with AI-driven analysis software and technologies data analysis becomes more streamlined and eliminates the chances of errors. Artificial intelligence is a sub-field of computer science, and datasets encompass machine learning and deep learning to analyze and interpret data.

AI in clinical trials plays a massive role, automating multiple tasks that consume most of the time during R&D processes. With the integration of artificial intelligence, researchers can easily track things such as drug identification, patient selection, data analysis, identification of drug patterns, and adverse impact of drugs. Therefore, AI in clinical trials shortens the time required for data analysis and significantly improves the success rate with real-time prediction. AI analytical tools can be used as prediction models to accelerate clinical studies such as disease identification and suitable patients and support clinical study designs. According to Roots Analysis the global AI in clinical trials market size  is estimated to be worth $ 8.5 billion in 2035, growing at a CAGR of 16% during the forecast period 2023-2035

Regulatory Status on Using AI in Clinical Trials

The U.S. Food and Drug Administration is highly committed to data to ensure drugs are effective and safe for human consumption. AI in clinical trials undoubtedly plays an essential role in fetching accurate data. Based on this, the FDA facilitates innovation in drug development and adopts a flexible risk-based regulatory framework that enhances technology innovation while safeguarding patient health. As a crucial part of bringing flexibility, In May 2023, the FDA’s Center for Drug Evaluation and Research has issued an initial discussion paper in collaboration with the Center for Biologics Evaluation and Research and the Center for Devices and Radiological Health. This paper has been published to address necessary considerations for using AI in clinical trials and drug development such as data quality, human-led government and model development standards. The regulatory authorities continue asking for feedback on the importance of advancing regulatory science in this field.

Applications of AI in Clinical Trials

AI in clinical trials helps to uncover valuable data insight during drug development that remains hidden. A clinical trial involves data analysis, patient recruitment, documentation and other data validation procedures that are crucial to get approval from the FDA.  AI-enabled technologies are therefore becoming a crucial part of the critical trials in the following areas.

1. AI in Clinical Trial Design

The adoption of AI in clinical trials by biopharma companies brings innovation to trial designs, effortlessly increasing the analysis of drug discovery data collected during clinical trials. AI-driven analytical tools help in quick comparison between current and past results of clinical trials. Integrating AI-enabled technologies also supports patient programs, post-market surveillance and has unparalleled potential to analyze, organize, or collect data generated during clinical trials. In simple terms, clinical trial design becomes more simplified and enables extraction of meaningful information such as drug failure, and adverse impact of drugs.

2. Site Selections

The most crucial aspect of clinical trials is the selection of highly functional investigator sites. Many parameters are taken into consideration when deciding investigator sites, such as administrative procedures, experienced clinicians, disease understanding, and resource availability. These qualities must be considered while selecting the site, as they influence data quality, integrity, and study timelines. AI in clinical trials helps pharmaceutical companies identify targeted sites and qualified investigators and collect evidence to meet regulatory standards. This ensures the clinical trial process adheres to Good Clinical Practice requirements.

3. Patient Enrichment, Enrolment and Recruitment

AI-driven transformation helps to improve patient selection and enhance clinical trial effectiveness through the proper analysis, mining, and interpretation of data through multiple resources. When combined with big data analytical tools, artificial intelligence helps interpret sources such as electronic health records and medical imaging data, thereby helping in patient recruitment.

4. Patient Medication Adherence, Monitoring and Retention

AI in clinical trials helps in patient medication monitoring and management by automating the capturing process. Combining AI algorithms with wearable digital technologies enables real-time insights and continuous patient monitoring. Moreover, AI algorithms also help fetch data about treatment effectiveness and safety while analyzing the risk of dropouts, enhancing patient engagement and retention.

5. Accurate use of Operational Data to Enhance AI-based Clinical Trial Analytics

Clinical trials generate a high amount of operational data. However, functional data disparate systems silos may hinder companies from receiving a comprehensive view of clinical trials portfolio across different global sites. Gathering information from any source into a common analytics platform backed with open data standards may promote integration while offering insights into important indicators. When combined with data visualization tools, a self-learning system intended to make better predictions and recommendations over time can proactively provide users with trustworthy analytics insights.

Limitations of AI in Clinical Trials

Though AI in clinical trials possesses enormous benefits, several limitations still have to be addressed. One of the potential limitations is the lack of standardization and data quality. Artificial intelligence relies on quality data to accurately predict and identify clinical trial patterns. Hence, the AI algorithm may not provide accurate prediction if the data is biased, inconsistent, or incomplete.

• Data security and privacy are major concerns when using AI in clinical trials. Patient data is crucial yet sensitive; it has been protected from being accessed by unauthorized parties. Clinicians must take appropriate steps to ensure complete privacy and security of patient data.
• Regulatory considerations have to be followed before implementation of AI in clinical trials. Though regulatory authorities such as the FDA have given flexibility in using AI for data analysis, using AI in clinical trials still raises concerns about data accountability and decision-making.

Paving Into the Future Vision of AI in Clinical TrialsTrails

Despite limitations, AI in the clinical trials market continually evolves to adopt and implement advanced technologies to improve data prediction while adhering to safety standards. The ongoing innovations show that AI in clinical trials holds a promising future.

Digital Twins Facilitate Real-Time Prediction

Digital twins, when combined with AI algorithms, generate virtual images that resemble patients’ physiological characters. This helps to achieve real-time insight into drug effects and individual health and enables the study of adverse drug impact. Currently, several pharmaceutical companies have begun to adopt digital twin technology monitored by AI to predict biological responses based on biomarkers.

AI Helps With Documentation

In the future, AI algorithms may expedite the regulatory documentation process and speed up the introduction of new drugs into the market.

Protocol Generation

AI language programs help create the first draft of clinical trial protocols using data inputs from previous trials, published literature, and multiple medical resources.

AI In Clinical Trials: Future of R&D Processes

Pharmaceutical companies are all set to pave into the storm of AI to develop tailored therapies that help treat diseases. As more novel drugs and therapies enter clinical studies, implementing AI in clinical trials becomes more feasible and important to shorten the time for drug development. Furthermore, flexibility in the regulatory guidelines for using artificial intelligence and machine learning in drug development encourages biopharma companies to accept innovation in clinical trials and implement cybersecurity to prevent data breaching issues.

In the future, AI / ML may combine with computer simulations and use advanced computer modeling in the regulatory evaluation of therapeutic drugs. Therefore, increasing implementation of virtual trials leveraging innovative digital technologies and artificial intelligence to lessen the financial burden and time required for drug development—bridging the gap between drug discovery and market supply.

Although AI hasn’t been included in clinical trials too often, it has the potential to revolutionize the process of developing new treatments. AI applications might make Clinical trials faster, safer, and far less expensive. The aim of biopharma to more thoroughly integrate patient-centricity across the whole R&D process will be achieved in part by the potential of AI to enhance the patient experience.

References

1. https://www.nature.com/articles/s41746-019-0148-3
2. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9974218/
3. https://www.forbes.com/sites/greglicholai/2023/10/04/
4. https://www.rootsanalysis.com/reports/ai-based-drug-discovery-market.html

About the Author:

Nancy Kapila is a seasoned pharmaceutical consultant with over 5 years of varied experience and a Master’s in Pharmaceutics from Panjab University. She excels in drug mechanisms and interactions. Her career highlights include collaborating with numerous pharmaceutical companies and offering strategic insights and guidance. Nancy stands out for her dedication to keeping abreast of pharmaceutical advancements, regulatory changes, and emerging trends. She believes in continuous learning to navigate the industry’s complexities and provide innovative client solutions. Fascinated by the role of data analytics in decision-making, Nancy delves into data to uncover patterns and opportunities, offering evidence-based recommendations for process optimization, product development, and operational efficiency. Her career is driven by a relentless pursuit of knowledge, passion for data insights, and commitment to leading pharmaceutical companies towards success in a dynamic industry.

Der Beitrag How Artificial Intelligence (AI) is Shaping Clinical Trials erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Meike Krautscheid – “The Synergy of AI & Blockchain – What are the Use Cases?”

The world of technology has experienced a whirlwind in recent years, driven by two overwhelming forces: the hype surrounding artificial intelligence and the meteoric rise in the realm of cryptocurrencies and blockchain. While the AI hype has been accompanied by rapid technological advancements and mass adoption through platforms like ChatGPT, the blockchain world, particularly the realm of cryptocurrencies, has seen its share of ups and downs. However, crypto enthusiasts eagerly await the next upswing.

While the once-dominant cryptocurrency hype was initially overshadowed by the unstoppable wave of artificial intelligence, a question arises:

How can the worlds of AI and blockchain harmoniously converge and potentially unleash synergy?

Within the blockchain community, the belief is widespread that the true magic of the technology unfolds when combined with other groundbreaking technologies. Alongside the Internet of Things (IoT), sensors, Smart Contracts, and Ricardian Contracts, Artificial Intelligence is coming under the spotlight. But before we delve deeper into the potential synergy between AI and blockchain, it is crucial to establish a foundation by understanding what blockchain is and what makes it unique.

Blockchain technology has revolutionized the way we can store and manage data. As early as the late 1980s, scientists Scott Stornetta and Stuart Haber recognized that the increasing flood of data would pose a challenge: the need to determine the time of data creation, authenticate it, and verify it to prevent fraud, such as tampering with transactions by backdating and editing.

The scientists’ approach was to use a kind of mathematical “blender” (cryptographic hash function) to generate a unique serial number, known as a hash, which is as unique as a fingerprint, for each file. This makes even the slightest change in a file detectable. Documents previously encrypted in data blocks using hash values and timestamps and chained together are resistant to retroactive alterations; only new data can be easily added.

Stornetta and Haber have been offering this system through their central company since the 1990s, allowing users to timestamp their files with a digital timestamp that proves the authenticity and integrity of the file at a specific time. This is a crucial tool for securing the integrity of electronic documents and data.

The innovation of a central timestamp system developed by Stornetta and Haber served as the template for the decentralized system in the Bitcoin blockchain. In Bitcoin, timestamping occurs in a decentralized network without the need for a central authority. Each transaction is hashed and protected by cryptographic keys. A protocol and consensus mechanism ensure that coins cannot be double-spent, and data cannot be retroactively manipulated. The order of transactions and blocks in Bitcoin is secured through the Proof-of-Work mining process. Even if multiple actors in the Bitcoin network fail, falter, or attempt dishonest actions, the system remains robust and continues to be a trusted, decentralized, and secure method for transferring value and data.

A decentralized blockchain is crucial for data integrity because it ensures that no central authority has the ability to retroactively manipulate data. Similar to a global accounting system, the blockchain updates its records simultaneously and decentralizes the origin of data. Moreover, it enables transparent tracking of changes to the data, including the detection of manipulations.

How can these advantages of data security through blockchain now intersect with Artificial Intelligence?

High-quality datasets are essential for developing powerful AI models. AI entities require high-quality data to learn patterns and make accurate predictions or decisions. For example, when the Retrieval Augmented Generation (RAG) framework is employed to retrieve results from an internal source, a blockchain safeguard can be used to verify that the data assets returned are authentic and that the content extracted from these assets aligns with the original consensus against these assets. However, it’s important to note that this is not meant for everyday use, as it is highly costly and is suitable for specific critical cases, such as mortgage documents and financial statements. Think of it as two databases converging: the vectorized database from RAG and the blockchain decentralized database using a consensus mechanism that is widely accepted as the standard. Therefore, the synergy with blockchain could improve the reliability of training data for AI models and enable more effective use of AI in various applications.

With the rise of generative AI-generated digital content, the boundary between reality and fiction is growing increasingly ambiguous. It’s becoming difficult to determine which images and videos are genuine, technically manipulated, or entirely AI-generated. However, a potential solution arises: we can label media content, including Deep Fakes, with universal indicators and facilitate the verification of the authenticity of such content through a blockchain by storing a simple hash of the content. This technology can confirm that the content remains unaltered and genuine, whether it is stored or indexed in the blockchain, and it is verifiable by anyone.

The potential of the alliance between AI and blockchain can also be explored in areas such as the Internet of Things (IoT), financial markets, Smart Cities, supply chain management, personalized medicine, and more.

In the field of Supply Chain Management, the combination of Artificial Intelligence and Blockchain technology could enable the analysis of data while ensuring a seamless tracking of the origin and the entire product supply chain. Usually, such data is centrally stored in data lakes, and when it is, there is a risk of data manipulation or the possibility that information does not reach relevant stakeholders in the supply chain in real-time.

AI algorithms can validate data before it’s entered into the blockchain to ensure it meets predefined criteria and standards. Real-time fraud detection is also made possible as AI models continuously monitor transactions for anomalies, with the transparency of the blockchain ensuring secure recording. Furthermore, AI data analysis facilitates informed decision-making, providing valuable insights and predictive analytics. This benefits supply chain quality assurance and empowers consumers to verify the quality and authenticity of products – provided that producers grant access to this data.

In Smart Cities, AI agents (AIAs) or Convolutional Neural Networks (CNNs), in conjunction with data stored on the blockchain, could enable a more economical and resilient urban economy. This combination allows for real-time data processing, crucial for urban emergencies, traffic control, and improving citizens’ quality of life. Convolutional Neural Networks (CNNs) are relevant for analyzing visual data in Smart Cities, including traffic pattern recognition, environmental monitoring, and security applications, while AI agents can recognize patterns and make intelligent decisions, such as resource allocation.

Similarly, Blockchain and AI offer numerous advantages in the healthcare sector. Firstly, blockchain allows the decentralized storage and secure encryption of health data, protecting it from hackers and unauthorized access. Patients have control over who can access their data, and with the help of Zero-Knowledge Proofs (ZKPs), patients can share their data without revealing their identity and compromising their privacy. AI agents can then access this data, identify patterns, and make informed decisions. For example, if DNA data is available, it can be used to detect rare genetic diseases.

Furthermore, imagine an AI trading bot that operates on the blockchain without revealing its detailed workings but proves its effectiveness through Zero-Knowledge Proofs. The combination of machine learning (ML) and yield farming also takes place on-chain, with crucial parts of the process remaining confidential. Blockchain enables verification and transparency of information, with critical parameters protected by ZKPs.

It’s worth noting that all transactions occurring on the blockchain can be traced using analytics tools. For example, the blockchain intelligence company Gray Wolf Analytics provides a tool that uses artificial intelligence to understand on-chain and off-chain activities. If fraudulent transactions are detected, financial and cybercriminal activities can be prevented or traced by relevant authorities.

There will also be a revolution in the software sector, as modern NoCode super-app builder platforms will be used to create apps, APIs, and websites with the help of AI. While AI initiates software creation, the use of blockchain creates a secure and verifiable environment for bug-free versions that can be verified by any user.

In another scenario, AI could serve as a “sheriff” monitoring punctuality to meetings. If someone arrives late, the AI triggers a Smart Contract on the blockchain, resulting in a donation from the tardy person to charitable projects. However, there is a certain risk associated with the use of these technologies, especially in authoritarian states concerning the monitoring of legal violations, as individuals’ identities could potentially be listed on a social rating or blacklist on the blockchain, leading to significant restrictions.

Blockchain technology could potentially address issues that come with the use of AI. In the context of Generative Artificial Intelligence (GAI), a challenge is that it might use copyrighted content to generate new content, potentially leading to conflicts with copyright owners. By utilizing digital signatures and hash functions, data integrity is significantly improved, allowing for cryptographic verification that a data record existed at a specific point in time and remained unchanged.

In a later article, we will delve deeper into how blockchain ensures transparent tracking of the creation and modification of content, addressing the legal aspects related to GAI and potential copyright infringements.

We can expect that in the future, blockchain will help distinguish between good and bad data. While blockchains offer ideal attributes for storing critical data, which can be a valuable data source for AIs, it’s important to consider that models like the Generative pre-trained Transformer (GPT-3) were trained on approximately 45 terabytes of text files – a massive amount of data. Given that storing data on the blockchain incurs monetary costs, it’s likely that only indexes like pointers or the most essential data will be directly stored on the blockchain for use as data sources for AIs. Economic and other incentives will be crucial with blockchain usage. Beyond a minimum of revenues that must occur, there are additional challenges to overcome, including scalability, interoperability, and legal issues like GDPR.

It’s worth noting that both AI and blockchain technologies are still in their developmental stages, but we can anticipate exciting developments and innovations on the horizon, with numerous opportunities yet to be explored.

About the Author:

Meike Krautscheid is an entrepreneur and expert in blockchain-based applications. Her extensive knowledge of blockchain, NoCode, AI and related technologies has established her as a recognized thought leader. Meike is a sought-after keynote speaker at international conferences and events. Furthermore, she shares her expertise and vision through lectures and workshops at renowned universities worldwide. Through her dedication, she engages with a worldwide audience and plays an active role in spreading innovations.

Der Beitrag The Synergy of AI & Blockchain – What are the Use Cases? erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: hbr.org – “How AI Can Help Leaders Make Better Decisions Under Pressure”

More and more businesses are turning to AI-powered technologies to help close the data-insight gap and improve their decision-making capabilities in time-critical, high-pressure situations. These technologies encompass a wide range of tools, including virtual assistants, virtual and augmented reality, process discovery, task mining, and an array of data analytics and business intelligence platforms. Recently, there has been tremendous interest in generative AI or large-language models, a whole class of algorithms that are able to ingest vast tracts of data — text, numbers, software code, images, videos, formulas, and so on — understand their probabilistic structure, and create summaries, answers, simulations, and alternative scenarios based on these data. This article addresses three critical questions faced by decision-makers in using these technologies: 1) In what contexts are AI decision-making technologies likely to be beneficial? 2) What are some of the challenges and risks of using these technologies? and 3) How can business leaders effectively benefit from these technologies while mitigating the risks?

Business leaders and managers face increasing pressure to make the right decisions in the workplace. According to research by Oracle and Seth Stephens-Davidowitz, 85% of business leaders have experienced decision stress, and three-quarters have seen the daily volume of decisions they need to make increase tenfold over the last three years.

Poor decision making is estimated to cost firms on average at least 3% of profits, which for a $5 billion company amounts to a loss of around $150 million each year. The costs of poor decision making are not just financial, however — a delayed shipment to an important supplier, a failure in IT systems, or a single poorly managed interaction with an unhappy customer on social media can all quickly spiral out of control and inflict significant reputational and regulatory costs on firms.

Against this backdrop, more and more businesses are turning to AI-powered technologies to help close the data-insight gap and improve their decision-making capabilities in time-critical, high-pressure situations. These technologies encompass a wide range of tools, including virtual assistants, virtual and augmented reality, tools for process discovery and task mining, and an array of data analytics and business intelligence platforms. Recently, there has been tremendous interest in generative AI or large language models (LLMs), a whole class of algorithms that are able to ingest vast tracts of data — text, numbers, software code, images, videos, formulas, and so on — understand their probabilistic structure, and create summaries, answers, simulations, and alternative scenarios based on these data. Well-known generative AI models include OpenAI’s ChatGPT, Google’s Bard, Meta’s Llama 2, and Anthropic, but there are many more.[…]

Read more: www.hbr.org

Der Beitrag How AI Can Help Leaders Make Better Decisions Under Pressure erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.