Archive 31.05.2026

The rapid expansion of soft robots and smart electronic devices is driving demand for materials that can not only move and adapt, but also complete their missions without leaving behind unwanted traces. As these technologies are increasingly explored for health care, environmental monitoring, infrastructure inspection, and security applications, robots and devices are expected to operate in places where human access is limited—such as narrow pipes, sealed spaces, underground facilities, and hazardous environments.

As traditional chip miniaturization slows, researchers have found a way to pack more computing power into the same space by stacking silicon circuits in multiple layers. The new process uses ultra-thin silicon membranes and low-temperature manufacturing techniques to overcome a major obstacle that has long blocked the production of true 3D chips.

A new room-temperature quantum device uses twisted light to entangle photons and electrons, overcoming one of the biggest hurdles in quantum technology. The breakthrough could pave the way for smaller, cheaper quantum systems with applications ranging from secure communications to future AI and computing platforms.

Roboteon has a highly differentiated solution, leading the way in the warehouse automation market: Interoperability across various WMS and Robotic platforms; The sector's most complete and functionally-rich platform, from core integration to advanced execution, optimization and orchestration of work; Broad use of AI and advanced simulation to improve decision-making and execution; Deep domain expertise, with special insight into integration of robots to Warehouse Management Systems and existing automation

From the shallow shores of Lake Wahlberg to the salty depths of the ocean, University of Florida researchers are dropping robots in the water and training them to communicate more efficiently in murky conditions.

SUTD researchers have developed a reinforcement-learning-based safety system that teaches a stair-traversing service robot to brace itself mid-fall, addressing one of the biggest barriers to deploying autonomous robots on staircases.

In robotics, there’s peak performance, and there’s everyday performance. The strain of scaling from the controlled pilot environment to the uncontrolled stress of real-time operations is where many deployments fall apart.

Claire chatted to Ahti Heinla from Starship Technologies about their AI-powered delivery robots that operate independently on streets and pavements.

Ahti Heinla is the co-founder and CEO of Starship Technologies, the world’s leading autonomous delivery company building AI-powered robots that operate fully independently in real-world environments. One of the original engineers behind Skype’s billion-dollar success, Ahti later made a quiet pivot into robotics, spending the past decade advancing practical, consumer-facing AI. Under his leadership, Starship has completed more than 10 million autonomous deliveries with a fleet of over 2,700 robots navigating streets, pavements, weather, and people, without human intervention.

AI in Pharma Industry

AI in Pharma: Innovations and Challenges

Artificial Intelligence (AI) is a rapidly growing technology that is used for a wide range of applications across industries. Small, mid-sized, mid-sized, and multinational companies are using AI technology and enhancing their capabilities to work smart in this digital sphere.

Like retail, e-commerce, and manufacturing sectors, AI is gaining prominence across healthcare and pharma sectors. Leveraging the power of this modern Artificial Intelligence in Pharma Industry, the companies are finding innovative ways to resolve some of the significant issues that the pharma sector is facing today.

Yes. AI-powered apps using machine learning, deep learning, predictive analytics, and big data have brought a radical shift in the paradigm of pharma.

Artificial intelligence in Pharmaceutical Industry has the potential to promote innovation, while at the same time increasing productivity and providing better results. In addition, Artificial Intelligence in Pharma Industry offers a value proposition to the companies by creating new and latest business models.

You can observe AI implementation in almost every aspect of the pharmaceutical field. From drug discovery and development to drug manufacturing to supply chain and marketing, AI has its impact. Hence, AI in Pharmaceuticals and Healthcare ensures cost-effectively operations, business efficiency, and hassle-free approvals for new drugs. We learn more about benefits of artificial intelligence in pharmaceutical industry as well.

 

In this article, we would like to give you a brief overview of the top 10 AI applications in the pharmaceutical sector. These best AI trends & use cases in pharma will let you understand the rapid AI adoption in pharma.

Let’s discuss

[contact-form-7]

The Best Applications Of Artificial Intelligence In Pharmaceutical Industry

#1 Drug Discovery Process and Design

The use of AI in the pharmaceutical industry for the design and development of drugs is increasing. From making small molecules to determining novel biological targets, AI plays a prominent role in drug target identification and validation. It is widely used for multi-target drug innovation and biomarker identification in an efficient way with great accuracy.

A major benefit of the pharma industry is that when AI is administered during drug testing, it minimizes the drug development time. Artificial Intelligence in Pharma Industry will also benefit drug developers to accomplish clinical trials faster and launch their products into the market for use. It leads to a cost and time-saving development process and also makes the innovative drugs available for improving patient care without side effects.

For example, researchers in pharmaceutical can identify and verify novel cancer drugs using data such as longitudinal EMR records (Electronic Medical Records) and other omic data. The AI systems using ML and other data analytics algorithms will extract insights from EMR data and creates the best formulations to design and develop drugs that cure tumors well.

#2 R&D

Pharma companies across the globe are using advanced AI-powered tools and ML algorithms to smoothen the drug research, development, and innovation process. These technology tools are designed to detect complex patterns in large datasets. Therefore, AI in pharma industry can be used to resolve problems associated with the research and development process.

This ability to study patterns of various diseases and to determine which composite formulations are best suited for the treatment of specific symptoms of a particular disease is excellent. Pharma industries can invest in the R&D of such drugs that are more likely to treat a disease or medical condition successfully.

#3 Disease Prevention

Pharmaceutical organizations can use Artificial intelligence to develop medicines Parkinson’s and Alzheimer’s and very rare diseases.

As per Global Genes, it is a fact that almost 95% of rare diseases do not have more drugs to treat and cure faster. However, thanks to the innovative capabilities of AI and ML. The use of AI in the pharmaceutical industry will completely transform this scenario and ensure the most-advanced models for detecting hazardous diseases in the early stage and improve patient outcomes.

#4 Next-Level Diagnosis 

Physicians can use advanced machine learning systems to gather, process, and analyze patient health care data. Healthcare professionals across the globe are using deep learning and ML to securely store patient data in the centralized storage system or cloud. It is called Electronic Medical Records (EMR).

Physicians may refer to these health records when they need to understand the effect of a specific genetic trait on a patient’s health or how medicine treats it. Machine Learning systems can use data stored in EMRs to generate real-time estimates for diagnostic purposes and to indicate appropriate treatment for the patient.

As ML technologies are capable of processing and analyzing large amounts of data quickly, they can help speed up the diagnostic process, thereby saving millions of lives.

#5 Epidemic Prediction

Pharma companies and healthcare industries are using ML and AI technologies to monitor and assess the spread of infections worldwide. These modern technologies are used for consuming data collected from various resources, analyzing several environmental, biological, and geographical factors on the population health of diverse geographical regions, and deriving data insights to reduce the impact of epidemics in the future.

Artificial intelligence and machine learning models are particularly beneficial for underdeveloped economies that lack medical infrastructure and financial framework to combat the spread of infection.

A good example of this is the ML-based malaria outbreak prediction model, which serves as a warning tool for malaria outbreaks and helps health care providers take the best action to combat it.

 

#6 Identifying Clinical Trials 

It is one of the key pharmaceutical use cases for embracing AI into existing models. The use of AI in the pharmaceutical industry for identifying drug candidates which are under final clinical trials from vast clinical data is on the rise.

Artificial Intelligence in Pharmaceutical Industry will help companies in analyzing thousands of samples in minutes and automatically logs data related to how patients are responding during clinical trials.

Here are a few advantages of using AI in pharma industry for clinical trials:

• AI applications or systems analyze historic clinical data
• AI apps help in monitoring drug performance and evaluating drug responses
• With the integration of speech recognition technologies, AI apps for pharma will be helpful for recording patients’ oral text during drug trial phases. It means that AI applications will record patients’ responses.

Hence, the use of artificial intelligence in clinical trials has the potential in fastening clinical trials and introduce the safest drugs into the market. It is also one of the top use cases for Machine Learning in Pharma. Speech analysis and real-time patient and drug monitoring activities will be done accurately using ML, deep learning, and natural language processing technologies.

 

#7 Drug Adherences and Dosage

The adoption of AI in Pharmaceuticals and Healthcare is increasing at a rapid pace for identifying the right amount of drug intake to ensure the safety of drug consumers. AI technology will monitor patients during clinical trials and suggest the right amount of dosage at regular intervals.

These are all key pharmaceutical use Cases for Embracing AI. AI in Pharmaceuticals and Healthcare will definitely accelerate automation in processes and drive more accuracy than ever before.

These AI trends & use cases in pharma will assist drug development and healthcare companies in ensuring efficacy across end-to-end production lines and delivering top-notch performance in front of the FDA.

 

Conclusion

The scope of Artificial intelligence and machine learning in the Pharma industry looks very promising in the future. AI opportunities for pharma companies are unmeasurable.

The use of AI applications in pharma will ensure operational excellence across drug structure design, drug development processes, selecting patients for clinical trials, monitoring drug performance, identifying proper dosage, etc.

Are you looking to hire an AI Development Company for your AI application?

Our AI consultants and developers will guide you on the right path!

 

Let’s discuss

[contact-form-7]

At this year’s Google I/O, the atmosphere at the Shoreline Amphitheatre was electric as the CEO laid out a vision for a world deeply intertwined with agentic AI. We heard about the sheer computing muscle of the new TPU 8i […]

The post The War on Deepfakes: How Google’s C2PA Integration at I/O 2026 is Fighting Back to Protect Our Reality appeared first on TechSpective.

Scientists at the USC Viterbi School of Engineering have developed a robotic hand that can hear a melody once and play it back after just two minutes of self-taught practice on a keyboard, without relying on sheet music or preprogrammed scores.

Cyborg insects have long been studied as bio-hybrid systems that combine living organisms with small electronic devices. These systems may one day support tasks such as disaster search and rescue, environmental monitoring, and sensing in spaces too small or dangerous for conventional robots. However, most existing systems control insects based mainly on externally visible behavior, such as whether the insect is walking or stopping.

Mechanical hands dexterous enough to thread a needle, childlike dancing robots and adult-sized ones to help with deliveries were on display Thursday as the Humanoids Summit Tokyo opened.

For us, Automte less about showcasing products and more about engaging in engineering discussions, such as managing duty cycles and thermal limits, addressing torque versus size constraints, and evaluating control architectures.

MIT engineers and colleagues have developed a soft, flexible gel that dramatically changes its conductivity upon the application of light. This figure shows a soft, stretchable circuit created with a rectangular bar of the gel. A copper electrode is attached to the left. A stylus and associated metal network connects the electrode to three “stations” on the bar. Light has been shone on the first two stations, creating conductivity that turns on each station’s lightbulb. Because the third station has not been exposed to light it is nonconductive and the bulb is off. Credits: Image courtesy of the Wallin lab.

Consider the chief difference between living systems and electronics: The first is generally soft and squishy, while the latter is hard and rigid. Now, in work that could impact human-machine interfaces, biocompatible devices, soft robotics, and more, MIT engineers and colleagues have developed a soft, flexible gel that dramatically changes its conductivity upon the application of light.

Enter the growing field of ionotronics, which involves transferring data through ions, or charged molecules. Electronics does the same, with electrons. But while the latter is well established, ionotronics is still being developed, with one huge exception: living systems. The cells in our bodies communicate with a variety of ions, from potassium to sodium.

Ionotronics, in turn, can provide a bridge between electronics and biological tissues. Potential applications range from soft wearable technology to human-machine interfaces

“We’ve found a mechanism to dynamically control local ion population in a soft material,” says Thomas J. Wallin, the John F. Elliott Career Development Professor in MIT’s Department of Materials Science and Engineering and leader of the work. “That could allow a system that is self-adaptive to environmental stimuli, in this case light.” In other words, the system could automatically change in response to changes in light, which could allow complex signal processing in soft materials.

An open-access paper about the work was published online recently in Nature Communications.

A growing field

Although others have developed ionotronic materials with high conductivities that allow the quick movement of ions, those conductivities cannot be controlled. “What we’re doing is using light to switch a soft material from insulating to something that is 400 times more conductive,” says Xu Liu, first author of the paper and former MIT postdoc in materials science and engineering who is now an incoming assistant professor at King’s College London.

Key to the work is a class of materials known as photo-ion generators (PIGs). These can become some 1,000 times more conductive upon the application of light. The MIT team optimized a way to incorporate a PIG into polyurethane rubber by first dissolving a PIG powder into a solvent, and then using a swelling method to get it into the rubber.

Much potential

In the material reported in the current work, the change in conductivity is irreversible. But Liu is confident that future versions could switch back and forth between insulating and conducting states.

She notes that the current material was developed using only one kind of PIG, polymer (the polyurethane rubber), and solvent, but there are many other kinds of all three. So there is great potential for creating even better light-responsive soft materials.

Liu also notes the potential for developing soft materials that respond to other environmental stimuli, such as heat or magnetism. “We’re inspired to do more work in this field by changing the driving force from light to other forms of environmental stimuli,” she says.

“Our work has the potential to lead to the creation of a subfield that we call soft photo-ionotronics,” Liu continues. “We are also very excited about the opportunities from our work to create new soft machines impacting soft wearable technology, human-machine interfaces, robotics, biomedicine, and other fields.”

Additional authors of the paper are Steven M. Adelmund, Shahriar Safaee, and Wenyang Pan of Reality Labs at Meta. 

Read the work in full

Soft photo-ionotronics, Xu Liu, Steven M. Adelmund, Shahriar Safaee, Wenyang Pan & Thomas J. Wallin, Nature Communications (2026).