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A team of cyber-systems researchers, engineers, optical specialists and roboticists at Zhejiang University, in China, has developed a navigation system for quadcopter drones that gives them the ability to carry out precise flight maneuvers autonomously. As reported in the journal Science Robotics, the group developed a multi-pronged system that allows quadcopter drones to carry out complex aerobatic maneuvers autonomously, in a safe manner, both indoors and outdoors.

Before deploying robots, the production process´ suitability for automation must be carefully examined. Especially as robot projects are becoming more complex and efficient implementation is more important than ever, thoughtful planning is essential.

The development of increasingly sophisticated sensors can facilitate the advancement of various technologies, including robots, security systems, virtual reality (VR) equipment and sophisticated prosthetics. Multimodal tactile sensors, which can pick up different types of touch-related information (e.g., pressure, texture and type of material), are among the most promising for applications that can benefit from the artificial replication of the human sense of touch.

In a series of interviews, we’re meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. In this latest interview, we hear from Amina Mević who is applying machine learning to semiconductor manufacturing. Find out more about her PhD research so far, what makes this field so interesting, and how she found the AAAI Doctoral Consortium experience.

Tell us a bit about your PhD – where are you studying, and what is the topic of your research?

I am currently pursuing my PhD at the University of Sarajevo, Faculty of Electrical Engineering, Department of Computer Science and Informatics. My research is being carried out in collaboration with Infineon Technologies Austria as part of the Important Project of Common European Interest (IPCEI) in Microelectronics. The topic of my research focuses on developing an explainable multi-output virtual metrology system based on machine learning to predict the physical properties of metal layers in semiconductor manufacturing.

Could you give us an overview of the research you’ve carried out so far during your PhD?

In the first year of my PhD, I worked on preprocessing complex manufacturing data and preparing a robust multi-output prediction setup for virtual metrology. I collaborated with industry experts to understand the process intricacies and validate the prediction models. I applied a projection-based selection algorithm (ProjSe), which aligned well with both domain knowledge and process physics.

In the second year, I developed an explanatory method, designed to identify the most relevant input features for multi-output predictions.

Is there an aspect of your research that has been particularly interesting?

For me, the most interesting aspect is the synergy between physics, mathematics, cutting-edge technology, psychology, and ethics. I’m working with data collected during a physical process—physical vapor deposition—using concepts from geometry and algebra, particularly projection operators and their algebra, which have roots in quantum mechanics, to enhance both the performance and interpretability of machine learning models. Collaborating closely with engineers in the semiconductor industry has also been eye-opening, especially seeing how explanations can directly support human decision-making in high-stakes environments. I feel truly honored to deepen my knowledge across these fields and to conduct this multidisciplinary research.

What are your plans for building on your research so far during the PhD – what aspects will you be investigating next?

I plan to focus more on time series data and develop explanatory methods for multivariate time series models. Additionally, I intend to investigate aspects of responsible AI within the semiconductor industry and ensure that the solutions proposed during my PhD align with the principles outlined in the EU AI Act.

How was the AAAI Doctoral Consortium, and the AAAI conference experience in general?

Attending the AAAI Doctoral Consortium was an amazing experience! It gave me the opportunity to present my research and receive valuable feedback from leading AI researchers. The networking aspect was equally rewarding—I had inspiring conversations with fellow PhD students and mentors from around the world. The main conference itself was energizing and diverse, with cutting-edge research presented across so many AI subfields. It definitely strengthened my motivation and gave me new ideas for the final phase of my PhD.

Amina presenting two posters at AAAI 2025.

What made you want to study AI?

After graduating in theoretical physics, I found that job opportunities—especially in physics research—were quite limited in my country. I began looking for roles where I could apply the mathematical knowledge and problem-solving skills I had developed during my studies. At the time, data science appeared to be an ideal and promising field. However, I soon realized that I missed the depth and purpose of fundamental research, which was often lacking in industry roles. That motivated me to pursue a PhD in AI, aiming to gain a deep, foundational understanding of the technology—one that can be applied meaningfully and used in service of humanity.

What advice would you give to someone thinking of doing a PhD in the field?

Stay curious and open to learning from different disciplines—especially mathematics, statistics, and domain knowledge. Make sure your research has a purpose that resonates with you personally, as that passion will help carry you through challenges. There will be moments when you’ll feel like giving up, but before making any decision, ask yourself: am I just tired? Sometimes, rest is the solution to many of our problems. Finally, find mentors and communities to share ideas with and stay inspired.

Could you tell us an interesting (non-AI related) fact about you?

I’m a huge science outreach enthusiast! I regularly volunteer with the Association for the Advancement of Science and Technology in Bosnia, where we run workshops and events to inspire kids and high school students to explore STEM—especially in underserved communities.

About Amina

Amina Mević is a PhD candidate and teaching assistant at the University of Sarajevo, Faculty of Electrical Engineering, Bosnia and Herzegovina. Her research is conducted in collaboration with Infineon Technologies Austria as part of the IPCEI in Microelectronics. She earned a master’s degree in theoretical physics and was awarded two Golden Badges of the University of Sarajevo for achieving a GPA higher than 9.5/10 during both her bachelor’s and master’s studies. Amina actively volunteers to promote STEM education among youth in Bosnia and Herzegovina and is dedicated to improving the research environment in her country.

Generative AI in Healthcare: Use Cases and Future Outlook

Generative AI originates content or information through Artificial Intelligence (AI) technologies based on learning knowledge from prompts. In recent years, incredible applications of the generative AI have been found within several sectors, primarily in healthcare as well. This article discusses the integration benefits of generative AI in healthcare, use cases, challenges, return on investment, and the outlook for the future.

Use Cases of Generative AI in Healthcare

• Drug Discovery
  Generative AI basically comes up with novel molecular structures based on surveying huge chemical data after which it predicts those efficacy. This enhances the speed in drug discovery through rapid identification of promising compounds so that the expensive and time-consuming processes of making new drugs go down.
• Medical Imaging
  Generative AI generates highly detailed medical images and also supports the diagnosis of anomalies including tumors, fractures, or any other pathology. Through enhanced image quality and resolution, generative AI helps deliver accurate diagnoses most of the time by identifying some problems that could easily be left unnoticed by a human eye; early disease detection is also ensured. 
• Artificial Data Generation
  Generative AI can make synthetic medical datasets that reflect actual patient data while maintaining their privacy intact. These synthetic data become great means of model training, research, and development of AI-enabled solutions in ways that preserve patient confidentiality, thus allowing more representative and robust studies.
• Virtual Assistants
  AI-powered virtual assistants give personalized health-related advices, reminders for medication intake, and answers to other health-related questions. Such assistants increase patient participation, provide persistent support, and lighten the burden of healthcare professionals by automatically responding to routine inquiries and administrative questions.
• Administrative Automation
  Generative AI can automate administrative time-consuming tasks like scheduling appointments, managing patient records, and billing. This minimizes the burden of administrative tasks on healthcare providers, increases the efficiency of operations, and helps medical professionals to spend more time on patient care.

Challenges in the Acceptance of Generative AI Models

• Data privacy and security
  It is a significant challenge as protecting sensitive patient information will be critical because generative AI needs extensive data sets. Such an operation could create serious data breaches and misuse of private health information. 
• Regulatory Compliance
  Following healthcare regulations like HIPAA (Health Insurance Portability and Accountability Act) and other regional regulations is a complicated task since generative AI tools must be extensively tested and pass rigid regulatory compliance requirements before deployment. 
• Bias and Fairness
  Generative AI models can inherit the biases present in training data, making the predictions unfair or inaccurate. These models have to be free of bias and equitable across the patient population being diverse. 
• Integration with Existing Systems
  This results in integration into existing systems where legacy technologies abound, making generative AI tool integration into well-established workflows complicated and often frustrating for healthcare providers. 
• Data Quality and Availability
  AI models operate best on structured data of high quality, and many health organizations face the problem of patient data being either incomplete or not consistent, leading to AI generating less accurate insights. 
• Less availability of expertise
  Generative AI needs healthcare professionals and data scientists with the right skills. Shortage in AI and machine learning expertise in healthcare has limited its adoption. 
• Implementation Cost
  Establishing and integrating generative AI will be very costly since it demands advanced technologies, infrastructure, and highly skilled employees, making it unaffordable to a considerable number of small-sized healthcare institutions.

Future of Generative AI in Healthcare

Generative AI, when combined with precision medicine and personalized treatment planning, will open new vistas into more accurate diagnostics, the ultimate being diseases predicted even before symptomatology begins to occur. Even surgical systems shall become much more precise and low risk and ensure a rapid return to life before the patient gets operated on with AI-based systems.

Generative AI will also have a great role in increasing the accessibility of healthcare to all corners of the globe, especially in underprivileged areas. Affordable quality care will be provided through AI-based remote consultation and diagnosis in resource-poor regions. Further, AI will lead to the more independent health systems where routine work is performed by AI and experts can deal with critical cases, thereby increasing the efficiency of the whole system.

Conclusion

Generative AI is a revolutionary factor within the health world, ranging from discovering drugs all the way through making personalized treatment plans, to say the least of making radiological imagines. Another problem that existed was with regards to data privacy. Often overcoming barriers brings additional regulatory burden with it. Here, higher return on investment for better efficiency besides cost-cutting in patient care was noticed. This is much more achievable when full integration and maturity through health care have taken place. Indeed, the future of healthcare is tied up with this promise of generative AI, which promises solutions to global health problems.

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A recently created RoboBee is now outfitted with its most reliable landing gear to date, inspired by one of nature's most graceful landers: the crane fly. The team has given their flying robot a set of long, jointed legs that help ease its transition from air to ground. The robot has also received an updated controller that helps it decelerate on approach, resulting in a gentle plop-down.

The Harvard RoboBee has long shown it can fly, dive, and hover like a real insect. But what good is the miracle of flight without a safe way to land?

If your application requires drive systems, stop by the maxon booth to see the latest in dc brushed, brushless and geared motor technology. We offer standard, semi-custom and fully customized drive solutions and our experts are there to help guide you.

For the first time, a drone has beaten human pilots in an international drone racing competition, marking a new milestone in the development of artificial intelligence. On Saturday April 14, 2025, two drone racing events took place simultaneously: The Falcon Cup Finals for human pilots and the A2RL Drone Championship for AI-powered, autonomous drones.

A team has developed an explainable AI model for automatic collision avoidance between ships.

Transform text-based prompts into high-resolution eight-second videos in Gemini Advanced and use Whisk Animate to turn images into eight-second animated clips.

How Much Does it Cost to Develop A Chatbot App?

Chatbots are intelligent tools that interact and respond to customers’ queries like humans. These save a lot of time and money invested in providing 24*7 customer services.

Using Artificial Intelligence (AI), Natural Language Processing (NLP), and Machine Language (ML) languages, Chatbot apps instantly interpret user queries and respond in seconds. Further, the emotional intelligence of AI in bots detects the feelings behind the user messages and generates a matching response.

An online survey states that by the end of 2025, over 80% of businesses are expected to deploy chatbots apps. Hence, there is a high demand for chatbots, especially across eCommerce, healthcare, financial, insurance, travel, and telecom Industries.

Types of Chatbot Applications

It is important to differentiate the various types of chatbots available in the market to choose the appropriate one for your business. Here, we have listed three major types of chatbot apps that are best to integrate into existing mobile apps or web-based applications.

• AI-Based Chatbots

These are an advanced type of chatbots that uses the Artificial Intelligence and Machine Learning for self-learning. The bots upgrade the response system regularly depending upon the previous conversations. They ensure better customer satisfaction and provide a human-like conversational experience.

• Rule-Based Chatbots

The rule-based chatbots use if/then logic to provide helps to users in clarifying their queries. These bots detect certain keywords in the user questions and validate them with many other questions and pre-defined answers to give an accurate reply to users.

• Menu or Button-Based Chatbots

The structure of this type of chatbot is quite basic and widely used because of its simplicity. These bots offer several options in the form of a menu or buttons to the users and respond depending upon the user selection. However, these bots disappoint the users when the mentioned options do not match the query.

Best Features of Quick Responding Artificial Intelligence Chatbot Apps

The features included in the application rely on the needs of businesses. In this section, we have listed basic features that every business seeks to incorporate in their chatbots.

• Simple & Easy Interface

Keep an easy-to-use user interface or app design that can make your chatbots more engaging and appealing to the customers.

• Conversational Maturity with NLP

Chatbots understand the user queries or the conversation and provide accurate responses using the power of Natural Language Processing (NLP) technology.

• Chatbot with Emotional Quotient (EQ)

The emotional quotient (EQ) is an integral part of AI chatbots. It assists the bot in detecting emotions and tone in the customer query and responds appropriately like humans.  Hence, this feature provides a personalized user experience to customers.

• Geo Location

Tracking the location of customers will help the chatbots to give more relevant responses during the conversation. For instance, they can provide the contact number of the sales executive of a nearby branch to users.

• Free to Explore Solutions

Chatbots are capable of exploring vast data and find relative answers to customer problems. It can analyze the existing solutions from different sources for sending accurate responses.

• Payment system integration

This feature suits for e-commerce chatbots to create secured payment methods.

Are you looking out for a chatbot application for your business?

USM is the best mobile apps development company in the USA. We develop custom AI apps for enterprises. Our chatbot app development services fuel your business growth and reform the way you interact with your customers.  

Get a free quote for chatbot apps development project!

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Benefits of Interacting Chatbot apps for Your Business

• Better Customer Experience

The ultimate advantage of chatbots is that they respond 24*7 instantly and ensure improved customer experience.

• Sales Automation

Apart from answering questions, bots automate the end-to-end sales process. Chatbots are the approach for brands to strengthen relationships with customers.  By analyzing consumer purchasing patterns and search patterns, marketplace chatbots generate leads for businesses. In this way, they help businesses to engage customers and make more sales.

• Insight Into Customer Behaviour

Businesses can get valuable insights into customer behavior from chatbot conversations. Based on the customer queries, businesses can identify which products have more demand in the market. It helps to make market strategies for promoting goods and also optimize the chatbot content to provide the best answers.

Use Cases of AI  Chatbots in Business

• Booking Bots

You can book or schedule the appointment automatically with AI bots. When users book an appointment, it creates an event and sends reminders to both the customer and business representative. These bots are also available in the travel, recruiting, and entertainment industries.

• Customer Support Chatbots

Customer support is the basic service of chatbots, and you can find these types of chatbots on every website. The features like availability, live chat, and quick response make them an ideal tool for customer service.

• Chatbot Use Cases in Marketing

To generate leads, collect customer emails, simplify product prices to customers, most businesses are deploying chatbots across their marketing channels.

• Entertainment Chatbots

These intelligent chatbots improve audience engagement by giving personalized recommendations of events, shows, and movies.

Are you looking for chatbot apps developers?

USM has over two decades of industry experience in custom chatbot apps development. Get in Touch!

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Cost to Develop an AI Chatbot Application

Now you know about all the benefits and use cases of chatbots, it’s time to check the cost to build a chatbot application for your business. The approximate cost to develop your bot with minimum functionalities cost around $30,000 to $80,000.

This estimated cost might vary based on the app’s platform (Android, iOS, Windows), App User Interface, app features, development team size, and location of mobile app developers you choose.

Conclusion

Chatbots are exciting and innovative tools to upgrade the customer experience. These give instant responses to the users and engage them like humans.

Build your Business Chatbot with Top Chatbot Development Company

Being the best mobile apps development company, USM develops full-fledged chatbot apps within your budget.

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In this interview, Meikel Quaas, President at DieQua Corporation, discusses the significance of creativity in mechanical design, and its application in automation designs, and provides examples of its implementation in existing solutions.

A groundbreaking open-source computer program uses artificial intelligence to analyze videos of patients with Parkinson's disease and other movement disorders. The tool, called VisionMD, helps doctors more accurately monitor subtle motor changes, improving patient care and advancing clinical research.

DolphinGemma, a large language model developed by Google, is helping scientists study how dolphins communicate — and hopefully find out what they're saying, too.