Digital transformation has made its way to analytical science.

Analytical science is a broad field impacting industries across the globe. One area where analytical science is seeing a surge is biotherapeutics which demands working with complex molecules that are challenging to analyze. As a solution to address the pressing issues, automation in analytical science is the need of the hour.

Biotherapeutics works with large and complex molecules. While the large surface area of these molecules results in a high rate of interaction with the drug targets, this large size also interferes with characterization techniques. The main characteristics researchers look for in biotherapeutics are protein glycosylation analysis, protein aggregation analysis, host cell protein analysis, and biomolecular interaction analysis. Researchers use an array of techniques like hydrophilic interaction liquid chromatography, two-dimensional liquid chromatography, bio-layer interferometry, and mass spectrometry techniques. This where automation is critical. Through these techniques, the knowledge of product consistency, safety, and efficacy are communicated throughout the processes. Automation will ensure consistency in data which is the crux of in-depth characterization.

The COVID-19 pandemic has put a spotlight on the need to automate almost every significant industry, including analytical sciences. During the initial phase of the pandemic, laboratories across the world had to maintain a minimum number of people to be productive while following social distancing laws. As laboratory staff has limited time to monitor the processes, automation will reduce the workload.

The thought of automation is not new to this industry. Partially automated systems, that require human intervention, were used for specific applications. That stemmed the requirement for complete automation that could facilitate improvements to the practices like data reproducibility, data tracking, and optimizing human efforts.

Once there’s a unanimous vote on automation, the next big hindrance is to successfully incorporate it into existing workflows. Researches need to strategize the alterations in the current workflow to make space for the new technology. Organizations should also be prepared for budgetary revisions and training programs. While today’s automation is simple and hands-free, some cases might require instrumental knowledge. Hence, automation will become a gradual process in the laboratories.

Owing to technological advancements, laboratory automation also witnessing advanced instruments and software. The recent pandemic only leveraged the adoption of automation and boosted digital transformation. A range of new solutions like Thermo Scientific Vanquish UHPLC Loader and Termo Scientific inSPIRE Collaborative Laboratory Automation Platform, by Thermo Fisher Scientific, are adding sophistication to upstream sample preparation, downstream analysis, and scientific workflows with no human interference.

It’s time for analytical sciences to transform with automation, especially the large workflows related to biotherapeutics. For end-to-end development, automation tools need to be adopted rightly to make the effort worth it. Like any new technology, combining it with a traditional workflow will be challenging and a time-consuming process, but this will enable laboratories to start operating with minimal downtime.

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Java devs looking to explore or work in data science may need another language up their sleeves. Python and R are common Java alternatives for data science.

Java can handle large workloads, and even if it hits limitations, peripheral JVM languages such as Scala and Kotlin can pick up the slack. But in the world of data science, Java isn’t always the go-to platform.

The front end of data science has recently been dominated by the languages Python and R, says Vivek Ravisankar, CEO and co-founder of HackerRank, a developer skills platform. “Python and R are both open source and free to use, giving them both a rich ecosystem and plenty of support from academic communities.”

Java developers who plan to explore data science calculation may do well to learn a little Python and R.

R and Python basics

The R programming language has implicit benefits when it comes to data science. Developed by statisticians for statisticians, R was designed to make data analysis and statistics easier to do, said Maria Khalusova, developer advocate at JetBrains. R has a number of unique statistics packages, and its matrix calculation capabilities are quite strong compared to Java.

R is often praised for its rich ecosystem, specifically around data visualization and specialized statistical methods. It is popular among folks who started their careers in statistics and advanced analytics. R is a specialized language, however, and it has limitations.

As a general-purpose language, Python has an advantage over R, Khalusova said.

Python is more production-friendly, and it’s easier to learn — both for beginners and those who switch to it from other programming languages. That accessibility may be why Python has been able to grow its rich data science ecosystem so rapidly.

Python supports a number of advanced machine learning libraries and frameworks, such as scikit-learn and TensorFlow. Python is also backed by the mature SciPy stack, which includes NumPy, SciPy, Matplotlib and pandas. This makes it well-equipped for numerical and technical computing. Its appeal for data science is how quickly developers can get started with Python. “For data science experts looking to start writing application code, this is the most straightforward route,” said Simon Ritter, deputy CTO of Azul Systems, which develops Java runtimes.

Java’s role

While Java, as well as Kotlin and Scala, can be used for data science, it’s more likely to play a role behind the scenes, Ravisankar says.

“Java is not built for data science — most Java applications were built for web servers and large-scale distributed applications,” Ravisankar says. “Java is statically typed and strictly follows the object-oriented paradigm.”

In contrast, Python follows a multiprogramming paradigm, which makes it easy for developers to write concise code using syntactic sugar. Python was not built specifically for data science workloads, but it does include many features that make it easy to code against data science workloads such as read-eval-print loops, notebooks and math libraries.

The community and tools around Python and R have continued to grow, further cementing their lead in data science coding.

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A new review published in the Journal of Research in Science Teaching highlights the potential of machine learning — a subset of artificial intelligence — in science education. Although the authors initiated their review before the COVID-19 outbreak, the pandemic highlights the need to examine cutting-edge digital technologies as we re-think the future of teaching and learning.

Based on a review of 47 studies, investigators developed a framework to conceptualize machine learning applications in science assessment. The article aims to examine how machine learning has revolutionized the capacity of science assessment in terms of tapping into complex constructs, improving assessment functionality, and facilitating scoring automaticity.

Based on their investigation, the researchers identified various ways in which machine learning has transformed traditional science assessment, as well as anticipated impacts that it will likely have in the future (such as providing personalized science learning and changing the process of educational decision-making).

“Machine learning is increasingly impacting every aspect of our lives, including education,” said lead author Xiaoming Zhai, an assistant professor in the University of Georgia’s Mary Frances Early’s Department of Mathematics and Science Education. “It is anticipated that the cutting-edge technology may be able to redefine science assessment practices and significantly change education in the future.”

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Researchers at Imperial College London claim to have developed a new machine-learning algorithm that could improve the design and performance of lithium-ion batteries and fuel cells.

They claimed that the algorithm would also allow researchers to understand the microstructure of fuel cells better. This will help run simulations that could enable them to improve the performance of these battery technologies, they noted.

Lithium-ion batteries are the most commonly used in electronic devices and vehicles. A fuel cell, on the other hand, is a device that converts chemical energy into electrical energy using oxidizing agents through an oxidation-reduction (redox) reaction. Improvements in these technologies would have widespread implications across several product sectors.

In their paper, the researchers explained that the performance of fuel cells is dependent on their microstructure and how the pores inside their electrodes are shaped and arranged. This determines how much power fuel cells can generate and the speed at which they can be charged and discharged.

The research paper published in the npj Computational Materials journal said that the algorithm could help reduce the volume of electrochemical simulations required to test the performance of a particular microstructure design during optimization.

The technique called the “deep convolutional generative adversarial networks” (DC-GANs),” will enable researchers to visualize and explore these pores virtually through three-dimensional (3D) simulations.

“Our technique helps us zoom right in on batteries and cells to see which properties affect overall performance. Developing image-based machine learning techniques like this could unlock new ways of analyzing images at this scale,” said Andrea Gayon-Lombardo, lead author of the paper from Imperial’s Department of Earth Science and Engineering

Previously, Japanese researchers said they have developed a new electrode material that they claim will make lithium batteries cheaper, more stable, and capable of holding more charge for longer periods.

Earlier, Researchers at Penn State University claimed to have developed a lithium-ion battery that is safe and has power and can last up to one million miles. A team of researchers at the Penn State’s Battery and Energy Storage Technology (BEST) Center developed the battery.

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Today, Intel and the National Science Foundation (NSF) announced award recipients of joint funding for research into the development of future wireless systems. The Machine Learning for Wireless Networking Systems (MLWiNS) program is the latest in a series of joint efforts between the two partners to support research that accelerates innovation with the focus of enabling ultra-dense wireless systems and architectures that meet the throughput, latency and reliability requirements of future applications. In parallel, the program will target research on distributed machine learning computations over wireless edge networks, to enable a broad range of new applications.

“Since 2015, Intel and NSF have collectively contributed more than $30 million to support science and engineering research in emerging areas of technology. MLWiNS is the next step in this collaboration and has the promise to enable future wireless systems that serve the world’s rising demand for pervasive, intelligent devices.”
– Gabriela Cruz Thompson, director of university research and collaborations at Intel Labs

Why It’s Important: As demand for advanced connected services and devices grows, future wireless networks will need to meet the challenging density, latency, throughput and security requirements these applications will require. Machine learning shows great potential to manage the size and complexity of such networks – addressing the demand for capacity and coverage while maintaining the stringent and diverse quality of service expected from network users. At the same time, sophisticated networks and devices create an opportunity for machine learning services and computation to be deployed closer to where the data is generated, which alleviates bandwidth, privacy, latency and scalability concerns to move data to the cloud.

“5G and Beyond networks need to support throughput, density and latency requirements that are orders of magnitudes higher than what current wireless networks can support, and they also need to be secure and energy-efficient,” said Margaret Martonosi, assistant director for computer and information science and engineering at NSF. “The MLWiNS program was designed to stimulate novel machine learning research that can help meet these requirements – the awards announced today seek to apply innovative machine learning techniques to future wireless network designs to enable such advances and capabilities.”

What Will Be Researched: Through MLWiNS, Intel and NSF will fund research with the goal of driving new wireless system and architecture design, increasing the utilization of sparse spectrum resources and enhancing distributed machine learning computation over wireless edge networks. Grant winners will conduct research across multiple areas of machine learning and wireless networking. Key focus areas and project examples include:

Reinforcement learning for wireless networks: Research teams from the University of Virginia and Penn State University will study reinforcement learning for optimizing wireless network operation, focusing on tackling convergence issues, leveraging knowledge-transfer methods to reduce the amount of training data necessary, and bridging the gap between model-based and model-free reinforcement learning through an episodic approach.

Federated learning for edge computing:

Researchers from the University of North Carolina at Charlotte will explore methods to speed up multi-hop federated learning over wireless communications, allowing multiple groups of devices to collaboratively train a shared global model while keeping their data local and private. Unlike classical federated learning systems that utilize single-hop wireless communications, multi-hop system updates need to go through multiple noisy and interference-rich wireless links, which can result in slower updates. Researchers aim to overcome this challenge by developing a novel wireless multi-hop federated learning system with guaranteed stability, high accuracy and a fast convergence speed by systematically addressing the challenges of communication latency, and system and data heterogeneity.

Researchers from the Georgia Institute of Technology will analyze and design federated and collaborative machine-learning training and inference schemes for edge computing, with the goal of increasing efficiency over wireless networks. The team will address challenges with real-time deep learning at the edge, including limited and dynamic wireless channel bandwidth, unevenly distributed data across edge devices and on-device resource constraints.

Research from the University of Southern California and the University of California, Berkeley will focus on a coding-centric approach to enhance federated learning over wireless communications. Specifically, researchers will work to tackle the challenges of dealing with non-independent and identically distributed data, and heterogeneous resources at the wireless edge, and minimizing upload bandwidth costs from users, while emphasizing issues of privacy and security when learning from distributed data.

Distributed training across multiple edge devices: Rice University researchers will work to train large-scale centralized neural networks by separating them into a set of independent sub-networks that can be trained on different devices at the edge. This can reduce training time and complexity, while limiting the impact on model accuracy.

Leveraging information theory and machine learning to improve wireless network performance: Research teams from the Massachusetts Institute of Technology and Virginia Polytechnic Institute and State University will collaborate to explore the use of deep neural networks to address physical layer problems of a wireless network. They will exploit information theoretic tools in order to develop new algorithms that can better address non-linear distortions and relax simplifying assumptions on the noise and impairments encountered in wireless networks.

Deep learning from radio frequency signatures: Researchers at Oregon State University will investigate cross-layer techniques that leverage the combined capabilities of transceiver hardware, wireless radio frequency (RF) domain knowledge and deep learning to enable efficient wireless device classification. Specifically, the focus will be on exploiting RF signal knowledge and transceiver hardware impairments to develop efficient deep learning-based device classification techniques that are scalable with the massive and diverse numbers of emerging wireless devices, robust against device signature cloning and replication, and agnostic to environment and system distortions.

About Award Winners and Project Descriptions: A full list of award winners and project descriptions can be found in “Intel and National Science Foundation Announce Future Wireless Systems Research Award Recipients.”

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In this era of artificial intelligence, it’s ironic that there’s so much that we don’t know about natural intelligence. But details of a missing chapter in the story of the brain are about to emerge from a new multi-million pound laboratory that will use wireless and wearable technologies to get inside the heads of toddlers.

The Wohl Wolfson ToddlerLab, part of Birkbeck University’s Centre for Brain and Cognitive Development (CBCD), is due to open in London’s Torrington Square in June. Inside, scientists will be able to scan the brains, monitor the gaze, and chart the hormone levels of one- to three-year-olds as they play in a series of real and virtual environments.

ToddlerLab will be a “world first,” says Denis Mareschal, director of the Centre, who has been working on the project for four years. “There is a black hole in our understanding of the development of toddler’s brains.”

It’s a successor to Birkbeck’s BabyLab, which has led the way in studying brain development since the CBCD opened in 1998. It revealed how babies can learn how the world works from surprising events, linking cause and effect. Researchers use eye tracking to explore what babies are thinking about, and demonstrate how they can figure out what mum or dad mean when they say the word “brick” by tracing their gaze to a piece of Lego. Sensor hairnets can register crackles of electrical brain activity as babies play on their parent’s laps, or fathom words in a stream of sounds (to a baby, all languages are foreign).

Among other projects, BabyLab scientists are helping to understand why people with Down’s syndrome do not get Alzheimer’s, studying the effects of screen time on babies as young as six-months, and seeking early signs of behavioural problems such as ADHD.

But toddlers, well, toddle so these methods have had to be adapted to study them in the new purpose-built lab, which abuts a Georgian house. Thanks to almost £40,000 of crowdfunding, £2.1 million from the Maurice Wohl Charitable Foundation and Wolfson Foundation, and £1.2 million from other backers, ToddlerLab will be equipped with wireless, wearable versions of motion trackers, hairnet sensors and functional near-infrared spectroscopy, in which light absorption is used to measure blood flow in the brain.

The lab includes realistic settings – a typical nursery and home – along with the CAVE, an immersive, VR environment that can recreate farm, supermarket or other surroundings. “Toddlers are active, curious and want to explore,” says Mareschal. “The lab will allow them to roam and behave as they would in the normal world.”

The lab enables researchers to see how children react in different circumstances, and – crucially – with other children present. Some disorders only emerge when toddlers interact with their peers. “Having lots of other children around brings out the difficulties these children have in engaging what others are thinking and how to respond,” he says.

A “biosamples collection suite” will take urine and other samples to study hormones such as cortisol, which is linked with anxiety, and oxytocin, which is released during social bonding. And a “nap lab” will monitor the effects of sleep on brain activity and learning.

The team want to use their new suite of tools to track the extraordinary changes in toddlerhood, when fatty sheaths of myelin boost the ability of nerve cells to conduct signals, swelling the brain as a result. The number of synapses grows from 2,500 per neuron to 15,000 by the age of three and major changes occur in the frontal system, which is central for intelligence, problem solving and organisation.

To investigate, ToddlerLab will focus on how toddlers manage multiple goals. His team will study toddlers as they build houses using Lego, watching the emergence of the critical frontal control system and revealing new insights into behaviours that range from “the terrible twos” to being able to speak fluently, solve problems and other core ingredients of intelligence.

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Social isolation and loneliness are concerns for many older adults, and can be triggered by the need to transition to a condo, rental accommodation, long-term care facility or retirement home. Sometimes, the only thing standing between an older adult and loneliness may be a beloved pet. This reciprocal relationship of affection and attention between human and non-human animal translates into physical and mental health benefits. However, in many cases, pets can’t move with their older adults since very few jurisdictions guarantee the right to bring an animal into a rental unit or condominium.

My research asks: what are the factors that impact well-being in older age? I explore the impacts of technology on privacy, autonomy and well-being, as well as the effects of the human-animal bond on health and well-being. I am also interested in whether social robots, including robopets, can produce the same effects.

In-home surveillance

Whether moving to a long-term care facility or a smaller home, many older adults find themselves subjected to increasing surveillance. Well-meaning family and caregivers install cameras, sensors and other devices to monitor independent older adults.

Social robots are new tools in the care of older adults. Some provide health-related services such as medication reminders, but most try to make up for the absence of human and animal companionship. These robots have artificial intelligence that is designed to interact with and provide comfort to the user.

For example, ElliQ is a small table-mounted device that interacts with a screen to enable “family members to easily check in.” It also interacts with the user, suggesting activities, responding to their voice or touch or look. ElliQ is always on, collecting data on the user that is transmitted to the manufacturer. Jennie is a robot dog controlled by voice commands and through a smartphone app. Robot pets, like other social robots, are designed to respond to the user’s emotions and, to do so, it engages in constant surveillance.

The responsiveness of social robots and robot pets relies on sensors to detect emotional responses, record emotions and forward this information to be analysed by algorithms that inform the robot’s response. The results of the data analysis prompt the robot to smile or purr or snuggle. In the case where a health response is required, some robots can inform the caregiver of elevated blood pressure.

Collecting personal data

Every step of this data process involves personal and sensitive information about an individual. For example, user identification data might be leaked at the sensing layer or in the cloud where the data is analysed to determine the right response. User profiles contain not only identifying information such as name and address, but also data gathered on user moods, behaviours and habits.

Between the potential for data exploitation and “the ubiquitous use of cameras and voice monitoring equipment in a home environment, there are privacy concerns that can affect human mental health.”

Older adults are not always aware of the extent of the monitoring, which can lead to feelings of shame and humiliation if, for example, a person is caught on camera singing, dancing, engaging in sexual acts or crying. It is not that older adults don’t realise that there is monitoring equipment, it is the 24*7 always-on aspect that may be unfamiliar to them. While enabling older adults to live with less human or animal contact, these monitoring systems and robots can increase their exposure and vulnerability.

Peer networks

The increasing number of surveillance-based options for providing care and companionship to older adults also ignores the reality that there are often other older adults who want to contribute meaningfully to their community. Using technology to build opportunities for human-to-human or human-to-animal interactions is one way to increase well-being without sacrificing privacy. For example, during virus season, technology could be used to develop something as simple as a telephone tree so that people can check in and connect in times of social distancing.

For older adults who are comfortable with technology, there are programs like Tech Buddies, which offers basic tech tutorials, including lessons on iPads, tablets, computers, laptops, smartphones and overall features of social media. And of course, the internet can be used to find services available to assist seniors with tasks around the house, to find nursing care or even friendly visitors. Other internet-based programs have been developed to support older adults to “age in place” with their companion animals. For example, Pet Assist, in Calgary, Canada, will help older adults manage pet-related tasks at home. After all, patting a robot dog is not the same as cuddling with a beloved pet. And a real dog will never tell anyone that you danced around the house in your underwear.

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The Korea Institute of Science and Technology Information (KISTI) and the Data Science Lab of Myongji University have selected the 10 most promising technological fields by using big data and artificial intelligence. The fields expected to show a very rapid growth until the mid-2020s include autonomous driving, energy, machine vision, biotechnology and robotics. The prediction is based on their deep learning-based future prediction model with an accuracy of over 86 percent.

They used 16 million pieces of data published worldwide for the past 12 years in developing the prediction model. The data was classified into 4,500 subject categories and AI and deep learning techniques were employed for quantification by category of network structure data, research content and research fields.

The 10 fields include renewable energy storage and conversion for hydrogen energy utilization. This technique for using hydrogen in fuel cells by producing it from water electrolyzed by renewable energy is expected to contribute to renewable energy storage and greenhouse gas emissions reduction.

The other fields include the development of advanced and eco-friendly air conditioning and heating system materials. Examples of the materials expected to contribute to greenhouse gas emissions reduction include nano-adsorbents for use in adsorption air conditioners and heaters, which are predicted to replace electric air conditioners and heaters.

Carbon dioxide capture and utilization, in the meantime, is to capture carbon dioxide and turn it into resources for use in biofuels, chemical products, construction materials, and so on. It can result in added value creation in various forms as well as carbon reduction.

Vehicle control technology development for autonomous driving improvement is to better control vehicle behaviors and ensure safety by recognizing fast-changing traffic situations with more accuracy and precision. It is data processing performance enhancement and intellectualization that are key to the development.

AI-based machine vision can be defined as automated decision making based on image acquisition and processing. These days, the scope of application of this technology is expanding very rapidly with the development of deep learning-based image processing and classification techniques and Industry 4.0 technologies such as smart factory operation.

Ultra-high-performance concrete development is to improve the salt resistance and durability of concrete and better prevent its carbonation so that buildings and structures can be used for extended periods.

Biodiversity research is a field comprehensively covering species exploration, research on interactions between organisms in the same habitats, research on genetic variations related to genes and individual organisms, etc.

High-voltage direct current transmission is to convert produced AC power into DC power, transmit it at a high voltage, and then supply electric power after reconversion into AC power. It is an advanced power transmission technique ensuring stability and a decrease in power loss and the demand for it is soaring with regard to cross-border power grid construction, renewable energy system linkage, etc.

Humanoid robot development is to work on controllable humanoids, including two-legged robots, so that they can do various jobs in place of humans. In this field, intellectualization is in rapid progress as to incident recognition, determination and prediction, hazard avoidance, and so on.

Lastly, hyperspectral imaging is to allow an object or a substance to be distinguished or detected with greater ease by acquiring spectrum data on a fragmented band by image pixel. Nowadays, it is developing at a rapid pace in combination with ultraspectral imaging, machine learning-based big data analysis, micro image sensors, and the like.

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In a new report, the joint big data task force of the European Medicines Agency (EMA) and the Heads of Medicines Agency (HMA) have proposed ten priority actions for the European medicines regulatory network to evolve its approach to data use and evidence generation, in order to make the best use of big data to support innovation and public health.

Big data can complement the evidence from clinical trials and fill knowledge gaps on a medicine as well as help to better characterise diseases, treatments and the performance of medicines in individual healthcare systems, the task force said.

The rapidly changing data landscape forces regulators to evolve and change the way they access, manage and analyze data and to keep pace with the rapid advances in science and technology.

“I look forward to working with the European Commission (EC) and national competent authorities to see how these concrete proposals can be implemented to better harness the potential of big data. This will help to further strengthen the robustness and quality of the evidence upon which we take decisions on medicines,” said Guido Rasi, Executive Director of the EMA.

The report highlights which of the ten recommendations it sees as priorities. This includes the establishment of an EU platform to access and analyse healthcare data from across the EU. This platform would create a European network of databases of verified quality and content with the highest levels of data security. It would be used to inform regulatory decision-making with robust evidence from healthcare practice, the report explained.

The joint task force has also encourgaed the development of skills to process and analyse big data within the network through training to enhance the capacity of regulators to assess applications for the authorisation of medicines that use big data sources as part of the evidence on benefits and risks. It proposes establishing a learning initiative to track and review outcomes of these types of submissions.

The report also emphasises the need to ensure data is managed and analysed within a secure and ethical governance framework and in active dialogue with key EU stakeholders including patients, healthcare professionals, industry, Health-Technology Assessment bodies (HTAs), payers, device regulators and technology companies.

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India is set to enter the hotly pursued domain of quantum technologies in a big way with a new National Mission on Quantum Technologies, according to a senior official of the Department of Science and Technology.

This area is significant to satellites, military and cyber security among others as it promises unimaginably fast computing and safe, unhackable satellite communication to its users.

Worldwide, governments, information technology giants and scientists have made this their thrust area and have invested big money and efforts into it, said K.R. Murali Mohan, Head of DST’s Interdisciplinary Cyber Physical Systems, New Delhi, in a recorded speech at an international scientific gathering at the Raman Research Institute here on Saturday.

Quantum technologies, he said, are strategically important and the inter-ministerial mission would involve ‘sensitive’ departments. China, the US and a few European countries are in the lead and India wants to join the scene in an era of highly damaging cyber attacks.

“The Government of India is also very much committed to developing these technologies. It is contemplating a National Mission on Quantum Technologies [by providing] huge investments through DST,” he said, inviting Indian scientists to provide insights to a detailed project report that is being prepared.

About 18 months back, the government initiated serious discussions in quantum technologies and kick started research projects across 51 organisations under QUEST – Quantum Enabled Science and Technology.

‘Not just research’

Dr. Murali Mohan said, “The NMQT will be a bigger mission than that with huge investments and widespread applications. We are in the stage of developing a DPR and [issuing] a memo on expenditure, finance and funding.” QT, he said, would not be just about research but aim to translate it into products and useful technologies.

The six-day meeting on ‘Quantum frontiers and fundamentals’ was hosted by the RRI’s Quantum lab, whose head Urbasi Sinha is leading highend work in quantum communication.

One of the world’s leading QT exponents, Jian-Wei Pan of the University of Science and Technology of China, told this newspaper that companies such as Google, Microsoft, Intel and IBM are intensely working on a ‘quantum computer’ that can crunch big data with ease: such a computer can crack 300-digit problems in seconds – while it would take today’s computers several thousand years to figure it out.

Other possibilities, he said, are precise time, position and magnetic field that will allow us to navigate without the GPS one day. “In future, this emerging area of QT can change information science as also our lives,” beyond what we can imagine now, he said.

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