I am a student at Nanyang Technological University, pursuing dual majors in Mechanical Engineering and Business. My academic and extracurricular pursuits reflect my commitment to exploring how cutting-edge engineering and innovative business strategies can drive advancements in motorsport, human-machine interaction, and connected technologies.
Formula 1 (F1) racing is a pinnacle of technological advancement in motorsport, where innovation plays a crucial role in enhancing performance, safety, and the overall excitement of the sport.
Two significant technological advancements in recent years are the Drag Reduction System (DRS) and the Kinetic Energy Recovery System (KERS). These systems not only improve car performance but also add strategic depth to races.
This paper delves into the workings, benefits, and implications of these cutting-edge technologies.
Drag Reduction System (DRS)
The Drag Reduction System (DRS) is a sophisticated aerodynamic feature designed to reduce drag and increase speed, facilitating overtaking manoeuvres during races. It consists of a movable flap on the rear wing of an F1 car.
When the flap is opened, it reduces the aerodynamic drag, allowing the car to accelerate more efficiently and gain a speed increase of up to 20 km/h.
DRS can be used during practice and qualifying sessions without restriction but only within designated activation zones during the race. These zones are typically located on long straights of the track, where the use of DRS is safest.
The system is activated when a trailing car is within one second of the leading car, measured at a ‘detection’ point before the DRS zone. This timing mechanism ensures that DRS is used strategically, promoting closer racing and more overtaking opportunities.
Safety Considerations
Safety is paramount in F1, and DRS usage is tightly regulated to prevent accidents. Corners are avoided as DRS activation points due to the increased risk of losing car control.
A stark reminder of the potential dangers associated with DRS occurred during the 2020 Bahrain Grand Prix, where Romain Grosjean crashed heavily after losing control of his car while using DRS in a corner. This incident underscores the importance of carefully selecting DRS zones and adhering to safety protocols.
Impact on Racing
DRS has significantly impacted F1 racing by making it more dynamic and exciting. By reducing aerodynamic drag, DRS allows cars to close gaps more quickly and execute overtaking manoeuvres more effectively.
This system has made races less predictable and more thrilling for fans, adding a strategic layer for teams and drivers as they decide when and how to best utilize the system.
Kinetic Energy Recovery System (KERS)
The Kinetic Energy Recovery System (KERS) is another revolutionary technology in F1, aimed at enhancing the car’s performance by harvesting and reusing energy that would otherwise be wasted.
KERS operates by converting kinetic energy generated during braking into electrical energy, which can then be stored and used to boost acceleration.
KERS consists of two motors located on the front wheels of the car. When the driver applies the brakes, these motors reverse their function and act as generators, converting kinetic energy into electrical energy.
This energy is stored in ultracapacitors, which are highly efficient storage devices. At the driver’s command, typically through a push button on the steering wheel, the stored energy is discharged back to the motors, providing an additional power boost for 6 to 8 seconds.
Benefits and Strategic Use
The ability to store and deploy energy on demand gives drivers a significant advantage, particularly during overtaking manoeuvres or when defending against an opponent. The short burst of additional power can make a crucial difference in maintaining or gaining positions during a race.
KERS also contributes to the overall efficiency of the car by making use of energy that would otherwise be lost during braking.
Environmental Impact
Beyond performance, KERS also represents a step towards more sustainable racing technologies. By recovering and reusing energy, KERS reduces the overall fuel consumption of F1 cars, aligning with broader environmental goals within the motorsport industry.
This technology is part of a broader push towards hybrid and electric systems in racing, showcasing F1’s role as a testbed for automotive innovation.
Conclusion
The Drag Reduction System (DRS) and Kinetic Energy Recovery System (KERS) are prime examples of how technological advancements in F1 not only enhance the performance and excitement of the sport but also contribute to safety and sustainability.
DRS enables higher speeds and more overtaking, making races more thrilling, while KERS improves energy efficiency and provides strategic advantages.
As F1 continues to evolve, these technologies will likely be further refined, maintaining the sport’s status as a leader in automotive innovation.
Most asked questions
Which zones are regarded as the safest for DRS?
These zones are typically located on long straights of the track, where the use of DRS is safest.
How do the motors function in KERS?
KERS consists of two motors located on the front wheels of the car. When the driver applies the brakes, these motors reverse their function and act as generators, converting kinetic energy into electrical energy.
How is KERS contributing to sustainability?
By recovering and reusing energy, KERS reduces the overall fuel consumption of F1 cars, aligning with broader environmental goals within the motorsport industry.
Most searched queries
Drag Reduction System (DRS)
Kinetic Energy Recovery System (KERS)
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India’s digital evolution has significantly advanced, moving beyond the foundational Aadhaar system (Digital India) to encompass a broader spectrum of digital innovations aimed at enhancing governance and service delivery.
The Digital India program, inaugurated in 2015, represents the backbone of this transformation. Its mission is to provide electronic access to government services, boost online infrastructure, and improve Internet connectivity. The initiative revolves around three main pillars: digital infrastructure as a utility for every citizen, on-demand governance and services, and empowering citizens through digital means.
Aadhaar: The Digital Identity Revolution
Aadhaar, the world’s largest biometric identification system, has been a cornerstone of India’s digital framework. Over the past decade, its role has expanded significantly, offering a reliable digital identity for Indian residents. This system has facilitated access to a variety of services, including banking, mobile connectivity, and government subsidies. By streamlining bureaucratic processes and minimizing fraud, Aadhaar exemplifies how digital solutions can enhance governance.
Unified Payments Interface (UPI)
The Unified Payments Interface (UPI) has revolutionized payment methods in India. Developed by the National Payments Corporation of India and supported by the Reserve Bank of India, UPI allows users to effortlessly transfer money between different banks. Its user-friendly design and efficiency have driven widespread adoption, positioning it as a key component of India’s fintech progress.
Digital Health Innovations
India’s health sector is also experiencing a digital transformation. The Ayushman Bharat Digital Mission aims to build a comprehensive digital health ecosystem, providing universal health coverage in an accessible and efficient manner. This initiative includes creating unique health IDs, maintaining digital health records, and enhancing interoperability within the healthcare system.
Advancing Education Through Digital Means
The National Education Policy 2020 (NEP 2020) underscores the role of digital technologies in expanding educational access and quality. Platforms such as Diksha, which offers a wide range of educational resources, and the National Digital Library of India, reflect the commitment to leveraging digital tools for educational improvement.
Connecting Rural Areas
The BharatNet project is working to deliver high-speed Internet to all Gram Panchayats, ensuring that digital services reach even the most remote areas. Complementarily, the Digital Village initiative aims to transform rural regions by enhancing technology access, digital literacy, and essential services, thus narrowing the urban-rural divide.
Addressing Challenges
Despite these advancements, challenges such as digital literacy, privacy issues, and cybersecurity threats remain. Addressing these concerns through policies like the Personal Data Protection Bill and improving cybersecurity measures and digital literacy programs demonstrates a commitment to overcoming these obstacles.
Over the past decade, India’s digital landscape has evolved remarkably. By fostering technological innovation, streamlining governance, and empowering citizens, these initiatives set a benchmark for digital transformation. India’s journey underscores the transformative potential of technology in shaping governance and society, paving the way for a digitally empowered future.
(The Writer is the editor of the oldest and largest Urdu newspaper of India, The Daily Milap (www.thedailymilap.com). He also the co founder of Global Order (www.globalorder.live), a foreign policy publication).
Most asked questions
How is Aadhar enhancing the governance?
By streamlining bureaucratic processes and minimizing fraud, Aadhaar exemplifies how digital solutions can enhance governance.
What is the aim of Digital Village initiative?
The Digital Village initiative aims to transform rural regions by enhancing technology access, digital literacy, and essential services, thus narrowing the urban-rural divide.
Most searched queries
Unified Payments Interface (UPI)
National Education Policy 2020
Personal Data Protection Bill
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Hello, I am Isabel. I am a rising junior in high school at Stamford American International School (SAIS). As AI-generated art rapidly gains popularity and advances, questions such as its ownership, copyright, and how it applies to existing intellectual property frameworks arise. Hence, I want to explore the different perspectives toward AI-generated art and how that should be applied to the law with my research article below.
Section 1: Understanding AI-Generated Art
If you are familiar with the concept of Artificial Intelligence (AI), you know that they are algorithm models that programmers train on extensive databases. They analyze vast amounts of data fed to them and learn patterns so they know how to respond to act. However, the technology itself cannot think on its own or create original ideas.
Likewise, AI-generated artworks are created through machine learning models. The databases of images and artworks they are fed with are often without the permission of their owners. With that said, how is AI-generated art affecting the artistic landscape, and where does this technology stand in terms of authorship and the legal status of AI as a creator?
Section 2: Popular Generative AI Websites
Let’s talk about some of the most popular AI generative websites. First off, is Midjourney. Launched in early 2022, it is one of the most recognized AI image/art generators. Though it has a $10-20 USD monthly subscription fee, it is known for giving the best quality pictures fast, which has attracted a substantial user base.
Leonardo AI, also launched in 2022, has a relatively beginner-friendly interface along with refined tools for paying professionals too. Its free version operates on a token system that resets on a daily basis, allowing users to generate images based on prompts. It produces images relatively well based on the prompts, and compared to Midjourney, it is more customizable because it has a larger variety of style options.
Leonardo AI Home Page (Source: Leonardo AI)
Ideogram, another AI generator that is gaining traction, also caters to a wide audience. It has a range of pricing plans catering to users of different needs. It has a free version where you are allowed up to 10 prompts a day. Its consistent performance in text-to-image generation makes it a reliable choice for many users.
Ideogram Explore Page (Source: Ideogram AI)
Dalle-E 3 is another popular image-generative AI in the market, particularly in AI artworks like drawings and paintings. Though it is still not fully developed, you can access it via ChatGPT Plus.
Notably, Dall-E 3 is one of the few AI image generators that states explicitly on its website that it is designed to refuse requests for images mimicking living artists’ styles. They also allow creators to opt their art out of future training of their image generation models.
However, challenges remain in ensuring artists receive recognition. Is a fake death certificate enough to make Dall-E 3 accept requests for mimicking a living artist’s style? What about the artists who are unaware that their work is being used to train AI models?
With that said, let us dive into the growing concerns about the unauthorized usage of artists’ art styles to train image-generating models.
Dall-E 3 Index Page (Source: OpenAI)
Section 3: Artist Recognition and Copyright Challenges in the Age of AI
Since 2023, the advent of AI generation tools has prompted many to be curious and experiment with them. This includes artists, and an increasing number of them are utilizing these tools to enhance their own work, with some feeding their own artworks into AI systems, so that after inputting a prompt, they are able to produce a new piece of art in their own unique style, all in a matter of minutes.
One wouldn’t exist without the other, and this raises questions about the relationship between traditional and AI-generated art.
In Singapore, the Copyright Act protects original creative work. The owner who made an original art piece is usually automatically given a copyright, and they are granted the right to reproduce, distribute, and display the work.
However, when it comes to work generated by AI, a critical question arises: Does the creator of the AI, the user of the AI, the work of artists who trained the AI, or the AI itself hold the copyright? The existing framework primarily addresses human authorship, leaving a grey area for AI-generated work.
As of now, the Singapore law does not explicitly recognize AI as the creator, which complicates ownership and copyright claims to their generated artwork.
Furthermore, Singapore’s legal system emphasizes that copyright protection is only given if the work is original. Since AI-generated art is often trained on databases with existing art, it raises questions about whether the art it generates meets the originality requirement. This could limit the ability of artists to claim copyright over AI-generated work that they incorporate their style or elements into.
As Singapore and countries around the world navigate these complexities, there is a growing consensus that intellectual property laws need to be updated to better fit the world today. Policymakers and legal experts should work together with artists and AI experts to shape a new legal landscape that protects creativity and authorship rights in this new age of AI.
Section 4: Protecting Artists – Tools and Views
Artists are increasingly turning to AI-generative-art poisoning software such as Nightshade, Glaze, Have I Been Trained, and more to protect their work from being used in unauthorized AI training.
These tools allow creators to alter the pixels of their artworks that are imperceptibly different from human eyes but will get picked up by machine learning algorithms and act as a poisoned data sample, resulting in deformed outputs from AI models.
Glaze More Info Page (Source: The University of Chicago)
However, the development of all these poisoning tools is not perfect, and most end up compromising the quality of the artwork. Additionally, some artists view the anti-poisoning software as another unnecessary step, given that another training model could come out to invalidate this approach.
Furthermore, another perspective on AI-generated art is that there is no difference between AI learning and an artist. Most artists take inspiration from other artists anyway and make their own art as a derivative of that.
AI needs artists, but if you look at human history, modern art is built upon thousands and thousands of years of art from different cultures across the globe. Artists need artists too, suggesting that AI could be seen as an extension of this creative process.
Contrary to that though, another valid argument is the idea that AI art is theft? It’s a common consensus that it takes at least 10,000 hours to learn a new skill. Most artists invest years practicing and honing their craft, while all companies do is download datasets of millions of artworks and use them to train their algorithm models.
With that, the models can then make combinations from what they “learned” and instantly generate pieces, often profiting from it through consumers without having done anything artistic themselves.
This situation is unfair to artists, as no human can feasibly digest billions of images and create amalgamations of all of them in seconds. This could lead to a culture of complacency, ultimately stifling creativity and innovation. It is concerning to consider that all “new” and “original” art could end in the next few years if lawmakers do not adequately protect artists’ rights.
However, a lot of this issue involves how AI interacts with the current intellectual copyright laws, further driving the need for policymakers to amend them.
As previously noted, law and legal experts should work together with artists and AI experts to consider adding something like an AI Commission system or requiring AI developers to get permission/credit from artists whose art is used in training datasets.
Conclusion
In conclusion, the rise of AI-generated art is both an opportunity and a challenge for artists and the legal system. Especially with the evolution of AI technology, education, and awareness are crucial for both artists and lawmakers to navigate this new landscape.
Legal frameworks need to reform and adapt so that artists, AI, AI creators, and users get fair recognition for their contributions. As we move forward, it becomes clearer that the ambiguity surrounding artistic authorship around the world has to be changed, fostering an environment where creativity can thrive alongside technological advancement.
Which are the commonly used AI-generative-art poisoning softwares?
Artists are increasingly turning to AI-generative-art poisoning software such as Nightshade, Glaze, Have I Been Trained, and more to protect their work from being used in unauthorized AI training.
How can I access Dalle-E 3?
Dalle-E 3 is a popular image-generative AI in the market. Though it is still not fully developed, you can access it via ChatGPT Plus.
Most searched queries
Midjourney
Ideogram
Leonardo AI
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Large Language models (abbreviated as LLMs) require enormous amounts of data for their training and retraining. Estimates suggest that Llama 3 was trained on a training set of 11 Trillion words, ChatGPT 4.0 in the meanwhile needed a paltry training set of 5 Trillion words !!
And that’s not all. Next generation models require data sets that are 10X larger… and so on.
While the possibilities with AI are infinite, we are hence heading towards finitism in the datasets that are needed to explore, and capitalize on, these infinite possibilities.
Why is data so important to AI?
Data is the oil for AI models. The reasons are well documented and can be summarized as follows:
Pattern Recognition: Machine learning and deep learning models rely on data to recognize and learn patterns, and then make predictions or decisions.
Training: Models use data to map inputs to outputs accurately, which is critical for tasks like classification, regression, and clustering.
Feature Learning: Data provides the features (variables) that the models need to learn from, identify features that are significant and their relationship to outcomes.
Performance Improvement: A large and diverse dataset helps the models learn a wide range of scenarios and variations, improving its ability to generalize.
Evaluation and Validation: Validation and test datasets are used to evaluate the models’ performance and ensure that it is not overfitting.
Bias Reduction: Adequate and representative data help in reducing biases in AI models.
Adaptation and Updating: Continuous data collection allows AI models to be updated and adapted, and hence continue to be relevant and accurate.
What are the current data sources?
If data is the oil for AI models, the current and known oil wells include the following:
The open data common crawl foundation: Consolidated from large scale web crawls, contains a data set of 25 trillion words, 55% of which is non-English. It is to be noted that these data sets are not de-duplicated.
Web data not captured by common crawl: Search engines such as Google/Bing, would have crawled a lot more data than common crawl. Much of this data would be long tail (restaurant menus for example) and not relevant for AI training. It is estimated that this could be 2 to 5 times more than the common crawl data set.
Academic publications and patent publications: Could probably add upto an additional 1 trillion words. It is to be noted however that much of it is PDF and requires OCR to extract text. Some of it is also behind paywalls.
Book archives such as Anna’s archive: Approximately 3 trillion words, most of which is PDF and behind paywalls/logins.
Can we do more to get more data?
Can we dig deeper to get more oil. Feasibly we can, however the law of diminishing returns catches up and a lot of what we would get, for example by more sophisticated web crawls will be long tail data which would not be relevant for AI models’ training.
Another solution is synthetic data. Synthetic data is artificially generated data that mimics real-world data, and is created using algorithms, simulations, or generative models. The challenges with synthetic data are the challenges of quality, validation and de-duplication.
There is hence a crying need for more oil/data. The immense possibilities of the AI industry is synergistic with this
Can data be created afresh – and how?
Can oil be created! In this case it very well can be. The treasure trove of oil, nay data , that AI companies are mining has been created by approximately 1% of the global internet populace. Global internet penetration cascaded from the more developed western world to the lesser developed regions over a period, hence the current data sets also suffer from biases, lack of representation and diversity.
The opportunity to create new data is immense. The global internet user base is approximately 5.4 billion. As a representation of scale of inherent knowledge that this global user base contains, a typical human being at the age of 20 has spoken 150 million words.
Estimates would suggest that the total number of words spoken daily, across languages and regions, is 115 trillion. Compensating for long tail irrelevance and duplication by a factor of 60%, we are still left with a useful super set of knowledge of 45-50 trillion words, daily.
This is the oil that feasibly needs to be created and then mined. The solution is to have a more significant portion of the worldwide internet populace to create this oil, nay data.
Incentivizing internet users to create data that AI models can use needs to be a gradual process that can leverage several levers, some of which are as follows:
Financial Incentives in the form of monetary rewards, profit sharing models offering data/content creators a share of the AI models’ profits, data marketplaces where data/content creators can sell their data/content.
Gamification in the form of points systems, leaderboards and badges, challenges and competitions.
Exchange of value in terms of access to subscriptions, tickets, events etal.
Recognition in the form of community building, recognising contributors and contributions, highlighting social impact, collaborative projects whereby contributors can see for themselves the results of their contributions.
Partnerships and collaborations with academia, academic institutions, AI researchers and corporates (both profit and non profit) that are building AI models.
Ensuring privacy of data and transparency and provenance on how the data/content contributions are being used.
This is a long road, but a mix and match of these approaches can create a compelling playing field for internet users to willingly and actively contribute their data.
If the data/content so created covers diverse scenarios and populations, the downstream models are less likely to suffer from bias, be more representative and diverse, more performant in decisions and more likely to perform fairly across different groups.
The data/content creation road has been traveled however, most notably by social media platforms. The platforms that take up data/content creation for the significant cause of the AI revolution should inculcate some best principles from the social media evolution, encyclopedias such as Wikipedia and Fandom, Ask me anything platforms such as Quora along with web3 principles of incentivization and decentralization. We owe this much to all the possibilities inherent to AI.
How many words are required to train present day LLMs?
Estimates suggest that Llama 3 was trained on a training set of 11 Trillion words, ChatGPT 4.0 needed a paltry training set of 5 Trillion words.
What is the average number of words we speak?
A typical human being at the age of 20 has spoken 150 million words. Estimates suggest that the total number of words spoken daily, across languages and regions, is 115 trillion.
How many people use internet?
The global internet user base is approximately 5.4 billion.
Most searched queries
Large Language Model (LLM)
ChatGPT 4.0
Optical Character Recognition (OCR)
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My name is Sneha Rani. Currently, I am pursuing B.Tech. in Electronics and Communication from the Indian Institute of Technology (BHU), Varanasi, India. I have keen interest in how large datasets are analyzed and transformed into meaningful results. The key of organisation and retrieval of data lies in data structures and algorithms. Now we are in a world where we have to use our energy to think of better solutions.
Data structures are everywhere!
In the current world where technology is embedded in our daily lives, the importance of data structures and algorithms cannot be doubted. Behind every app, website, and digital service is a large network of data structures and algorithms that are working day and night to make our lives more comfortable, efficient, and fun.
Indeed, the basic ideas of computer science such as the optimization of searching results and the powering of recommendation systems are the ones that are changing the way we interact with technology.
Introduction to DSA
The basic elements of computer science have changed the way we communicate with technology, thus, we could go beyond the possible results of physical labour and concentrate on problem solving which is more creative.
There are numerous data structures and algorithms such as the greedy approach, dynamic programming, graphs, trees, linked lists, arrays and lists, sorting and searching, just to name a few. Whatever you want to do, whether you want to optimize the solution or cut the cost, all can be done by data structures and algorithms in an efficient way.
Dijkstra and Bellman-Ford’s algorithms are designed to help you determine the shortest paths between two nodes while Floyd Warshall’s method is used to calculate the shortest path between each pair of vertices in a graph. Dynamic programming is a process that enables you to save the previous results and compare the new ones to discover the most efficient solution.
Trees can be employed to preserve the integrated structure of the data sets. Arrays give you the possibility to experiment with different dimensions which enables the processing of various kinds of data and operations. Linked lists facilitate you to use the storage efficiently and data can be stored dynamically.
The hashing can cut the search time by an exponential factor, thus, providing a good user experience. Stack and queue are the most beneficial data structures. Stack and queue are as straightforward as taking books from a pile and being in line at the ticket counter. The stack and the queue are used to solve many complex problems at once very easily.
With every dive into the world of data structures we discover its endless possibilities, we are intrigued by its complexities and intricacies, and we are drawn into its depth.
Let us consider some of the applications of data structures and algorithms:
Efficient Information Retrieval
Think of the situation of looking for information on the web without the support of good data structures and algorithms! It would be similar to looking for a needle in a haystack.
Through the use of data structures like hash tables, binary search trees, and algorithms like breadth-first search and depth-first search, search engines can quickly go through the huge amount of data to find the results which are relevant in milliseconds.
You can be looking for a nearby restaurant, researching a topic for a school project, or shopping for a new pair of shoes and data structures and algorithms will ensure that the information you need is in your hands in just a few keystrokes.
Personalized Recommendations
Have you ever realized that the ads on your favourite social media platform always seem to be perfectly tailored to your interests and that your Instagram and Tik-Tok feed feels so familiar? Or how streaming services fight for your viewing time by recommending movies and TV shows that match your viewing habits?
Data structures are the basis of the suggestions given to you by your online shopping app.
The possibility of such a high degree of personalization is due to the use of advanced recommendation algorithms that take into account your past behaviour, preferences, and demographic information to suggest the content that you are likely to enjoy.
Through the use of data structures such as graphs and algorithms like collaborative filtering and content-based filtering, technology firms can generate personalized experiences that make users to stay longer and come back for more.
Optimized Transportation and Navigation
Navigation apps have become vital for travelers who are going to their work, planning a trip, or exploring a new city. Behind the scenes, these apps use data structures like graphs and algorithms like Dijkstra’s shortest path algorithm to calculate the most effective routes, considering factors such as traffic jams, road closures, and real-time updates.
Through the optimization of the transportation routes, data structures and algorithms not only save time and fuel but also cut down on stress and make the whole travel experience a lot more pleasant.
Enhanced Communication and Collaboration
Nowadays, the world is more interconnected than ever, and communication and collaboration are key for both personal and the professional success.
Instant messaging apps, email clients, and collaboration platforms use data structures, such as queues, stacks, and trees, and algorithms, such as sorting and searching, for the fast and efficient delivery of messages.
No matter if you are texting a friend, sending files to coworkers, or attending a virtual meeting, data structures and algorithms make communication and collaboration possible even if the people are in different time zones or across distances.
Let us now delve into some real-life cases where data structures and algorithms are being used:
Artificial Intelligence and Machine Learning
Artificial Intelligence and Machine Learning are the areas in which these intelligent technologies are predominantly being used.
AI and machine learning algorithms usually use special data structures that are designed for data representation and processing, thus making it easier to carry out tasks more quickly and accurately.
For instance, decision trees are employed in classification tasks, while neural networks make use of complex graph-like structures to depict the relations between data points.
Databases
Databases are now everywhere in the present world, they are the engine that drives all from social media to financial systems.
Behind the scenes, databases depend on the complex data structures like B-trees, hash tables, and indexes to store, retrieve, and manage the huge amounts of structured data efficiently.
File Systems
The file systems are the ones that are responsible for the organization and management of the files that are stored on the computers and the storage devices.
Data structures such as linked lists, trees (like B-trees or binary trees), and hash tables are used to keep the file metadata, directory structures, and file locations on the disk.
Financial Systems
Financial systems handle huge amounts of transactional information and perform complex calculations.
Data structures such as priority queues, hash tables, and trees are used to carry out financial instruments, visualize market trends, and improve trading strategies.
Computer Graphics and Gaming
Data structures are the key factor in computer graphics and gaming, which are used to model and manipulate objects, scenes and game states.
For instance, spatial data structures such as octrees are being used for the collision detection and spatial partitioning.
Healthcare Systems
Healthcare systems keep the patient records, medical images, and the treatment plans using data structures like linked lists, trees, and hash tables.
The data structures thus, are the tools that make the organization of patient data, the tracking of the medical histories and the communication between the healthcare providers possible.
Social networks
Social networking platforms are dealing with heaps of user data and connections between users. Graph data structures are used to create social network models, in which nodes are users and edges are the relationships.
Graphs are then utilised to the algorithms which are employed to recommend friends, detect the communities, and analyse the network behaviour.
Competitive Programming
Competitive programming is somewhat like a sport for computer programmers, where the participants are the ones who compete to solve algorithmic and computational problems within a given time frame.
The primary goal of competitive programming is to write efficient and correct code to solve a variety of problems, usually with time limitations.
Competitive programming is a branch that deals with the best use of data structures and algorithms to solve the real world problems using the least resources for the best result.
This calls for a lot of brainstorming. Competitively, data structures are the key to tackling problems in an efficient and effective manner.
They are the tools that enable the users to arrange and manage data in an efficient way. Participants have to not only comprehend the operation of these data structures but also determine when and where to use them to solve various problems.
Conclusion
Through the process of data structures and algorithms, information retrieval is simplified, and personalized experiences are introduced to the real world which is helpful in our daily lives.
Using the basic principles of computer science, the developers and engineers can come up with ingenious solutions that make our life easy, convenient, and more enjoyable.
The ever-changing technology will make the relevance of data structures and algorithms increase even more, thus, leading to the advancements and changing the way we communicate with the world around us.
The central point is that data structures and algorithms are the basic elements of our digital society and thus they are the tools we use to overcome the modern world complexities with confidence and ease.
By adopting data structures and algorithms, we can open new doors, trigger innovation and thus, build the future that the next generations will live in.
References
Data Structures Using C And C++ by Y. Langsam, M. Augenstein And A. M. Tenenbaum
Which data structures are used for non-recursive implementation of programs?
Stack and queue are used to solve many complex problems at once very easily. They are the keys to implement non-recursive solutions of programs.
Which data structures are helpful in visualizing market trends?
Data structures such as priority queues, hash tables, and trees are used to carry out financial instruments, visualize market trends, and improve trading strategies.
Most searched queries
Collaborative filtering
Decision trees
Machine learning
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Written by Steve Shi onDigilah(Student Tech Research)
My Bio:
I am a 1st year student at Nanyang Technological University (NTU). Through this article, I aim to discuss the integration of Explainable Artificial Intelligence (XAI) in healthcare to address transparency concerns in AI decision-making. Delving into the world of XAI, the ability to explain and interpret the decisions made by AI algorithms could bridge the gap between technological advancements and the human touch in healthcare.
Introduction:
In recent years, the integration of Artificial Intelligence (AI) and Machine Learning (ML) in healthcare has surged rapidly, presenting revolutionary opportunities for improving diagnostics, drug discovery, and patient outcomes. However, the escalating reliance on complex AI systems has raised concerns about transparency, accountability, and the interpretability of decision-making processes.
In response to these challenges, Explainable Artificial Intelligence (XAI) has emerged as a key player in making AI systems more transparent and understandable, particularly in the critical realm of healthcare.
In the healthcare sector, where high level of precision, reliability, and accountability are demanded, traditional AI systems are often viewed as “black boxes,” as the internal workings and processes that lead to a particular output or decision are not easily understandable or interpretable by humans, raising doubts about their decision-making processes.
In healthcare applications where lives are at stake, the lack of transparency in AI decision-making processes can lead to critical implications. This concern is addressed by XAI by providing insights into how AI systems arrive at their decisions, offering a level of transparency that is essential for fostering trust and collaboration between AI and healthcare professionals. In addition, XAI goes a step further by offering transparent explanations for medical diagnoses, unravelling the intricacies of drug discovery processes, and demystifying the decision-making landscape in clinical settings.
Figure 1: Healthcare Professional & AI
Medical Diagnosis:
One of the primary applications of XAI in healthcare is in medical diagnosis. AI systems are increasingly utilised to analyse medical images such as X-rays, CT scans, and MRIs to aid in disease detection.
However, the opacity of these systems raises questions about their accuracy and reliability. XAI steps in by generating explanations for the decisions made by AI algorithms, enabling medical professionals to verify and understand the reasoning behind the proposed diagnoses.
This transparency not only enhances the trustworthiness of the AI system but also facilitates collaboration, leading to more accurate diagnoses and improved patient outcomes.
Figure 2: Overall Pipeline of medical XAI application[1]
Drug Discovery and Development:
The drug discovery and development process are areas where XAI is poised to make a significant impact. AI systems play a crucial role in sifting through vast datasets to identify potential drug candidates for various diseases.
However, the lack of transparency in these systems makes it challenging for researchers to comprehend how a specific drug candidate was identified. XAI addresses this by providing clear explanations for the decisions made by the AI system.
This transparency empowers researchers to understand the underlying mechanisms and rationale for the drug discovery process, ultimately leading to the development of more effective and targeted treatments. The result is a streamlined drug development process that saves time and resources.
Figure 3: Illustration of AI model used in the lab[2]
Improving Patient Outcomes and Reducing Healthcare Costs:
Predicting health risks and optimizing treatment plans are vital aspects of healthcare that benefit from AI systems. XAI plays a crucial role in this context by offering transparency and understanding.
By providing clear explanations for the predictions made by AI systems, clinicians can comprehend the reasoning behind a particular decision or recommendation, enhancing clinical confidence, promote the widespread implementation of AI-based clinical decision support systems, and ultimately result in improved patient outcomes and better healthcare delivery. [3]Healthcare professionals can also make informed decisions and interventions, contributing to early disease detection, preventive measures, and ultimately, improved patient outcomes.
Additionally, the transparency afforded by XAI can contribute to reducing healthcare costs by enabling more efficient resource allocation and strategic decision-making.
Challenges in Implementing XAI in Healthcare:
While the potential benefits of XAI in healthcare are immense, implementation comes with its set of challenges. As AI systems become more complex, finding the right balance between simplicity and accuracy, is essential to ensure that the transparency offered by XAI does not compromise the reliability of AI systems.
Additionally, standardisation and guidelines for implementing XAI in healthcare[4] are also needed to guarantee the transparency and reliability of the AI systems. Furthermore, ensuring that explanations align with human models is an also ongoing area of research.
The Future of XAI in Healthcare:
As the integration of AI in healthcare continues to evolve, the role of XAI becomes increasingly pivotal. Its potential to enhance transparency, trust, and collaboration between humans and AI systems has far-reaching implications for the industry.
Ongoing research and development in XAI will likely address current challenges and contribute to the standardisation of practices in healthcare. The future holds promise for XAI to revolutionise healthcare practices, ensuring that the benefits of AI are harnessed responsibly and ethically for the betterment of patient care.
Conclusion:
Explainable Artificial Intelligence is on the verge of transforming the landscape of healthcare. From enhancing the interpretability of medical diagnoses and drug discovery processes to improving patient outcomes and addressing ethical concerns, XAI stands as a beacon of transparency in the realm of AI applications.
The integration of XAI in healthcare promises a future where AI systems work seamlessly with healthcare professionals, leading to more accurate diagnoses, efficient drug development, and ultimately, improved healthcare for all. As the journey of XAI in healthcare progresses, it is essential to continue refining its implementation, ensuring that transparency and accountability remain at the forefront of technological advancements in the pursuit of a healthier future.
References:
Göllner, S. (2022, August 11). State of the art of Explainable AI in Healthcare in 2022. Medium.
Jin, W., Li, X., Fatehi, M., & Hamarneh, G. (2023). Guidelines and evaluation of clinical explainable AI in medical image analysis. Medical Image Analysis, 84, 102684.
XAI steps in by generating explanations for the decisions made by AI algorithms, enabling medical professionals to verify and understand the reasoning behind the proposed diagnoses.
What are the key benefits of integrating XAI into healthcare practices?
Integrating XAI fosters trust and collaboration, leading to accurate diagnoses, streamlined drug development and better patient outcomes.
Most searched queries
Magnetic Resonance Imaging (MRI)
Computed Tomography (CT) scan
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For three straight years global oil prices have averaged above $70 per barrel, bringing an end to the period of stable and low oil prices that prevailed over 2014-20. The evolving conflict in the Middle East threatens to keep prices elevated for even longer.
Historically, this would have been a cause of great concern for the global economy. In the 1970s, oil price shocks resulted in sharp increases in inflation and large decline in outputs. However, this is not the case anymore. The global economy has taken the latest jump in oil prices in its stride.
What explains this?
The resilience of global growth to high oil prices is not newfound. The real economic effects of oil prices have been diminishing since the early 2000s as major economies have become more energy efficient. This is demonstrated by their falling oil intensities, which means it takes less oil to produce a unit of GDP than before. In fact, much lesser.
This is true for India too. Its energy intensity has fallen 33% over the last twenty years and it is one of the least oil-intensive nations globally. While this is generally viewed as a positive development, it’s important to understand the underlying dynamics to ensure that the low intensity is sustained.
Government policies aimed at conserving energy and promoting energy efficient technologies such as LED lighting and high-efficiency appliances have contributed to this. India passed the Energy Conservation Act in 2001 and constituted the Bureau of Energy Efficiency a year later.
But India has also been less oil-intensive because of structural factors such as coal-based electricity generation, a relatively small manufacturing sector, low rate of urbanisation, high usage of biomass amongst rural households and low ownership of combustible engine vehicles.
With the government pivoting towards manufacturing and rising incomes leading to a transition towards a more urbanised economy, more automobile purchases and wider adoption of more efficient fuels like diesel and LPG, India’s per capita oil consumption has been on an uptrend and if left unchecked, could eventually result in higher oil intensity levels as well.
Economic development is essential. But it shouldn’t come at the cost of energy efficiency, given the negative impact of rising fossil fuel consumption on climate and India’s pledge to achieve net-zero carbon emissions by 2070. Keeping oil-intensity low is vital to the decarbonisation strategy.
So, what is the way forward?
Clean tech can play an important role in balancing India’s growth and environmental targets, while keeping oil demand in check. Here are some ways it can do so:
Enabling lower fuel consumption:
Wider adoption of electric and hybrid vehicles can significantly cut down oil demand. The government aims to have electric vehicles (EVs) make up 30% of new vehicle sales by 2030.
Improving efficiency:
Data analytics and AI can help end-users better manage their energy use and reduce their fuel bills. For example, AI can analyze data to predict traffic patterns and improve route planning, ensuring quicker and more fuel-efficient transport of people, as well as, goods.
Producing cleaner fuels:
Biofuels, which are produced from renewable organic materials, have emerged as a viable alternative to fossil fuels in the power and transport sectors. Consumption of ethanol-blended petrol consumption is on the rise in India. The country’s ethanol blending rate has jumped from 0.67% in 2012 to 12% currently and is targeted to rise to 20% by 2025.
Developing stable sources of alternate energy supply:
India has made good progress in incorporating renewables in its energy strategy. Non-fossil sources now account for 41.4% of installed power capacity. However, they account for just 26% of the electricity generated as renewable sources like solar and wind are weather dependent. Deploying battery storage solutions that store excess energy during peak production times for use when production is lower will help narrow this gap.
Aiding compliance:
Technology also aids in the enforcement of policies and regulations aimed at conserving energy and lowering demand. Digital monitoring systems can track emissions and energy use, ensuring compliance with environmental standards and supporting government efforts to transition away from fossil fuels.
In sum, the advantages of clean technology are undeniable and widely recognized. However, the path to adopting such solutions faces significant financial barriers. These challenges include high costs and limited availability of financing, which hinder the rapid adoption of these technologies and raise questions about the feasibility of the government’s targets.
For example, with EVs currently making up less than 5% of vehicle sales in India, increasing this to 30% by 2030 will require a monumental effort.
Despite these obstacles, it’s important not to overlook the progress being made. The International Energy Agency (IEA) estimates that the rise in EV sales and improvements in energy efficiency could reduce India’s oil demand by 40% over 2023-2030 compared to a scenario without these changes.
In the end, the strong political commitment to decreasing fossil fuel dependency, supported by ambitious targets and favourable policies, suggests that India is on a steady path toward a cleaner energy future and managing its oil consumption effectively.
Most Asked Questions
When did India pass the Energy Conservation Act?
India passed the Energy Conservation Act in 2001 and constituted the Bureau of Energy Efficiency a year later.
How AI and data analytics can help in using energy efficiently?
AI can analyze data to predict traffic patterns and improve route planning, ensuring quicker and more fuel-efficient transport of people and goods.
Most Searched Queries
International Energy Agency (IEA)
Carbon emission
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Hi, I am Quynh. I am currently a 3rd year Communication Studies major at Nanyang Technological University. Back in my first year of university, generative AI was largely an unknown topic. Fast forward to the present, it is the central point of every discussion about the use of technology in communication and media. Witnessing this swift change is the motivation for me to reflect on my experience with these tools and ponder the question: Is generative AI the future of my industry?
Read my research article below:
Generative AI (Gen AI) took center stage as the prominent buzzword of 2023. This technology revolutionized every single industry for its capacity to quickly generate various types of content including text, images, audio, and more. Its extraordinary capacities led to significant workforce changes and stood behind some of the major layoffs in the past year. That said, how is Gen AI transforming the advertising landscape, and where does it stand in an industry where it does what it does best – generating text and images?
The buzz around Generative AI
Gen AI first existed in the ’60s century in the form of chatbots. However, it was not until recent years that they became widely accessible and adopted by internet users worldwide. While Gen AI can assist all sectors in their day-to-day tasks, the implication of the current technology, which is to generate various types of media, remains the most relevant in the advertising industry.
In their original forms, text generators stand as a revolutionary tool for communication specialists and advertisers with their ability to churn out ad copy and refine written pieces in no time. Image generators are also widely utilized and proven to be a cost-effective and time-efficient alternative.
But the potential of Gen AI does not stop there. Big players in the tech industries are also bringing generative AI into their products. Adobe Photoshop, the household name for all professional creatives, introduced their AI-powered Generative Fill in early 2023. This feature was widely celebrated, as it helps to significantly reduce the time spent on creating and editing media assets.
Adobe Photoshop Generative Fill (Source: Adobe Support)
Canva, catering to non-professionals, also incorporated AI to swiftly assist users in discovering templates along with the AI image generator and other capabilities.
Canva’s Magic Studio, powered by AI (Source: Canva)
Google also introduced a new Gen AI tool for their Google Performance Max partners. This innovative tool will aid advertisers in asset creation, ad copy generation, and more.
Google’s AI-powered ads tool (Source: Google)
These tech giants saw a trend in advertisers’ habits: a desire for tools that facilitate swift content creation, allowing more time for other meaningful tasks. It is also a strategic move to cut the budget on outsourcing designers and writers and produce content in-house with the help of AI.
The confined box of AI tools
While Gen AI exhibits extraordinary capabilities, it fundamentally remains a product crafted by humans and acquires knowledge through the datasets fed into the system. Therefore, its generating capacity is limited by what is available in the training dataset.
A prominent example is a trend that blew up on TikTok. Taking advantage of the new image-generative feature of ChatGPT, users create prompts that ask AI to generate different sets of images. All went well until a user took a step further and asked ChatGPT to generate an image of a hamburger without cheese. To our surprise, AI failed at this simple task despite the creator’s effort to rephrase and work around the instruction.
The internet community went wild, with many joking about how “they said AI will take over the world”. But what is left after a good laugh is the acknowledgment that AI’s capacity is limited. As most hamburgers in the training dataset have cheese, AI faces difficulty creating one without it.
Generally, Gen AI tools’ capability is limited by specific tasks assigned to them and the quality of the data used for their training. They can only perform certain tasks or only have the knowledge up until when it was trained. Those who use Gen AI in their work must have a good understanding of the limits of this technology, use it to assist only in relevant tasks, and double-check the quality of the work.
How far is AI from us?
Just as advertising professionals must undergo training and years of experience to excel at their job, Gen AI also requires training to adapt to the specific tasks that it is assigned. It is undoubted that with enough time and investment, Gen AI will become even better at producing results and catering to specific industries, including advertising.
But in this specific case, the learning curve might be steeper for AI than it is for humans. The supporting arguments often center around two key aspects: flexibility and originality.
Text AI generators, most prominently ChatGPT, are known for their limited flexibility in writing. While they can be instructed to adopt different styles, some users remain unsatisfied or frustrated as the result does not precisely meet their expectations.
A simple instruction to ‘energize’ a piece of text may return a copy that is oversaturated with emojis and buzzwords. The absence of ‘empathy’ in current AI technology also hinders their ability to produce written pieces that effectively address the intricacies and nuances of communication tasks.
The conversation surrounding originality often involves copyright infringement. This concern originated from the fact that third-party material might have been used in the process without permission. While referencing past artworks is a common practice in the creative industry, the added touch of human creativity somewhat mitigates these concerns.
To conclude, the fact that Gen AI does not seem to have ‘creativity’ on its own will continue to pose concerns about its range of capabilities and the originality of the product created. Yet, Gen AI remains a powerful tool that can elevate human creativity by offering new ideas and bringing in new thought dimensions.
How will Gen AI help the advertising industry to evolve with time?
Most asked queries
ChatGPT
Canva
Adobe Photoshop
Copyright infringement
Generative AI
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In this article Danielle (Financial Investment expert) and Ajit (AR, VR and Web 3 expert) jointly explore:
Fintech Cyber Security dos and donts
Investment education on Finance 101
Below is a video by the writers themselves high lighting the key take aways from the writeup. Reach out to them if you want a 101 with them to learn more.
Danielle’s thoughts on Fintech Cyber Security
The need for cyber security has become paramount in today’s modern age, particularly in the fintech and financial space. Being in this industry myself, I handle sensitive client data daily, and have access to their online wealth accounts; it is therefore vital that their information stays safe and inaccessible to fraudsters. Robust security measures must be in place, and I am constantly having to upgrade and refresh my skills to keep my clients safe.
Whilst fintech has allowed for financial services to become more streamlined, convenient, and efficient, it has somewhat opened the floodgates for cyber-attacks and threats. Harvard Business Review reported a 20% increase in data breaches from 2022 to 2023, and this is set to increase further as the years progress. Not only does this mean we have to constantly upgrade our software and infrastructure, but human area can become a massive opportunity for cyber criminals. I truly believe that a two-pronged approach of new regulatory processes, along with using AI in cybersecurity is a dynamic tactic to tackle this ever-evolving problem.
Cyber security is now seeing the same level of regulation as every other type of security, which means that fintech companies in particular must adhere to stringent rules and procedures. Regulations such as the General Data Protection Regulation (GDPR) and the Payment Card Industry Data Security Standard (PCI DSS) must be followed. Whilst of course this is best practice to ensure that clients’ data is safe, it therefore adds an extra strain onto the company and its employees; this may lead to delayed admin processes, longer lead time for new business submission and therefore, a time delay in profit for the company. Time is money, and the longer it takes for profit to be made, it essentially means smaller margins for the company.
One way this can be tackled is with Artificial Intelligence. Whilst using manpower takes time and money (not to mention the risk of human error), AI systems can scan masses of data sets, analyse data, spot anomalies, and therefore detect possible cyber risks before they have even happened. This preventative method ensures that risks are managed efficiently, and before they become breaches, which means a safer system for the clients, and mitigates possible reputation risk for the company.
However, AI is not a final solution; with cybercriminals’ techniques ever evolving, it means that AI will have to do the same. Not only that, employees must keep re-training when new systems are introduced, to ensure that human error is kept to a minimum. Moreover, one must ensure that the third-party companies engaged to deliver this AI system, is also compliant, safe, and follows the stringent regulations set in place for fintech companies to adhere to.
But the buck doesn’t just stop with the company- clients and customers must also stay vigilant so that they don’t fall victim to cyber-crime.
For example, being able to spot a phishing email, not clicking on unknown links, and not giving out all your banking details to someone over the phone. In order for an individual to be savvy, particularly when it comes to fintech and online financial transactions, they must be aware of risks and know when and where it is appropriate to give out their financial information. If you engage a professional for your financial planning, of course you will have to make them aware of your personal details and possibly even bank details. But do take note that they should be encrypting or password-protecting any sensitive documents that are being sent to you.
Even if you are planning your finances alone, and are using platforms for your investing, be sure to do your own due diligence; ensure that the apps you are using are regulated and have secure payment systems. Do take note that most will require you to upload some form of identification, as well as declaring your tax residency. Whilst to a layman, this may seem intrusive, this is actually a sign that the platform is doing its part to adhere to compliance and regulations. If they don’t ask of these from you, it could be a sign that the platform is not regulated.
For those that plan their investing and finances alone, cybersecurity becomes an even bigger risk, as this is normally something that a large corporation would have to ensure the safety of first, but now it is being left to the individual investor. If you are considering planning your finances yourself, having basic understanding and knowledge is incredibly important.
Therefore, I often suggest that people understand four main areas before they start investing, which I will explore further in this article.
Over to my co-writer Ajit who introduces how metaverse and block chain technology will probably bring future solutions to curtail fraud in our highly susceptible finance industry.
Ajit’s thoughts on Fintech Cyber Security
Background
When we analyse the extent of online fraud and scams, it’s a bit bewildering! As per FTC in US, online scams tend to harm more young people than the elderly. In 2021, Gen Xers, Millennials, and Gen Z young adults (ages 18-59) were 34% more likely than older adults (ages 60 and over) to report losing money to fraud like online shopping scams as well as job scams. Most of the elderly, on the other hand, are victims of tech support calls duping them of their earnings. The median reported loss was $800 for people 70-79, and a whopping $1,500 for those 80 and over. On the other hand, the median individual reported fraud loss by people 18-59 was $500 in 2021.
As the fastest growing economy in the world, India is no stranger to online frauds. 62% of the frauds affect the age group 18-52 as per data from 2018. With robust infrastructure around UPI, this number is bound to decrease.
Blockchain Technology to the Rescue
With weakening currencies in countries like Zimbabwe and Venezuela and hackers from China and Russia, the attacks will only amplify, in the years ahead. There is an urgent need to safeguard individual financial earnings, leveraging technologies like AI and Blockchain. While they are large and independent technologies, they form a core part of the Metaverse. They are the processing as well as the security layer of the Metaverse. Many futurists have predicted that our interactions will be with digital twins of institutions and banks in the Metaverse.
Fast forward to 5 years from now and the permutations and combinations of frauds and financial losses for individuals will only amplify. The promise of Blockchain is to essentially safeguard the assets and investments of individuals as well as organizations. By utilising blockchain, banks can set up a secure and tamper-proof ledger of all financial transactions.
With real-time monitoring and instant access to transaction records across the blockchain, organizations can track and analyze transactions in real-time, allowing them to detect and prevent fraud as it occurs. The trust architected within the technology enables seamless detection.
Challenges with Emerging Technologies
As has been the scenario with any technologies when they are new, be it Television or Computers or even Gaming, new technologies take time to be accepted mainstream owing to numerous challenges. Some of the challenges with Blockchain technology are as follows.
Evolving Technology – Until a technology is adopted mainstream, the maturity of the technology is determined by its limited set of users. The technology is tested for various scenarios by the very same users. Much like the planets move across the solar system with time, in addition to their rotation and revolution, the world is ever evolving with all its volatilities, uncertainties, complexities and ambiguities. No system can be tested for robustness without the volume of usage which only comes with higher adoption. Blockchain technology needs to cross this bridge to deliver on its promises of safety, security, and robustness.
Data Privacy Concerns – The more data that’s visible to Blockchain (and AI), the more seamless the tracking of frauds. But, from a user’s perspective, it warrants sensitive data to be made available, traceable at all times. With GDPR norms in Europe as well as upcoming Data Privacy Bill in India, Blockchain as it stands today, seems to conflict with the regulations.
Energy Consumption and Infrastructure – With ESG goals being one of the focus areas across organizations and Governments, the carbon footprint recorded by emerging technologies like Blockchain and AI, with cloud-based high-compute, tends to be on the higher side. There is a need for hardware optimization to be able to leverage the technology to its potential, in an environmentally responsible way.
In conclusion, Blockchain technology will serve as the protective layer of the Metaverse and will be at the forefront of minimising frauds and innovations around it. There is a need to accelerate the adoption of the technology to ensure its robustness to enable us to face the challenges of Metaverse in time.
Over to my co-writer Danielle who simplifies investing basics and how your hard earned money can work harder for you.
Danielle on Finance 101
I have many clients and connections that I come across asking me for advice on how to get their finances in order. ‘How can we maximise what we have now, so that we can make the most of our money later?’. Of course, one of the best passive things we can do, is to invest.
Investing is the concept of allocating assets, usually money, into different financial vehicles to create a profit. The bare minimum investment should be doing is beating inflation, because over time our hard-earned money is worth less, due to the rising cost of products. Before one starts investing, it is best to have a clear strategy, and get the basics covered first. Here are a few key financial areas you should have planned for:
1. Build an Emergency Fund
At a glance investing may seem like an obvious choice when it comes to saving money. Why not just throw all your savings into investment if it means high returns? The answer is that investment returns are NOT guaranteed– even the safest investments come with some risk, and sometimes the lock in periods are high, or the penalty for withdrawing early is expensive. To ensure that you are not over-investing, make sure that you have an emergency savings fund that is easily accessible. That way should an emergency arise (like a large hospital bill or having to pay for car repairs), you can use your emergency money instead of jeopardising your investments.
The recommended amount you should have in your emergency fund is 3-6 months of your monthly salary. This should be a healthy buffer should the worst happen. If you already have more than that, then that’s a great time to consider investing.
2. Know How to Budget
Of course, setting aside for investment would be impossible if you didn’t know how much to set aside. That’s why organising your budget is a crucial step in your financial planning. There are many ways and methods for planning, but a good starting point would be the 50/20/30 rule:
50% of your monthly salary maximum should go on things you need to pay for: housing, bills, groceries & insurance.
30% can go on doing the things you enjoy: hobbies, drinks and travel.
20% should go into your savings: think about your long term savings and investment goals.
If you have surplus each month, you can even consider increasing this 20% to a higher proportion and allocate more into your investment goals.
3. Be Debt-Free
Before you do any investing, you should really consider paying off your debt. Having a credit card bill is fine, but having any large or bad debt will hinder you in your long-term goals. It seems counter-productive attempting to make lots of money with investments, whilst paying off lots of debt. It may be difficult paying off student debt or large loans, but you will reap the benefits in the long run when your debt isn’t eating into your assets.
4.Set Your Investment Goals
This is arguably the most important step, defining your goals. What is the reason for investing? If you are doing it out of pure greed, then your judgment will become clouded when it comes to riskier investments, and you risk losing it all. So have a long and hard think about why you want to invest. You are putting your money, that you worked hard for, somewhere that could give you high returns, or give you nothing.
Therefore, it’s best to have a long think and define some clear goals for your future. Do you want to plan for your retirement? Save for a house? Pass something on to your children? Whatever it is, decide how much you would need and by when. Most investments give better returns if you have a longer-term commitment, so it’s OK to think big. If you have no clue and are just investing for the sake of it, you will quickly lose your drive and passion for making money.
These steps may seem simple, but they really are the key to an effective investment strategy. I work with clients every day to ensure that they have budgeted correctly, serviced their debt, and built an emergency fund, and together we work together to work towards their financial goals. Many find that this is more complex than they first thought and will include tax planning and ensuring that their assets are protected. This is of course one of the added benefits of hiring a professional. If you feel that these services are something you would require, feel free to reach out at Danielle.teboul@sjpp.asia or click here.
Over to my co-writer Ajit who tells us that finance 101 is best learnt by engaging emerging technologies like AR, VR as it helps in educating about financial products to customers in a more engaging and impactful manner and to all age groups, across the economic strata.
Ajit on Finance 101
Background
In its “Economic Well-Being of U.S. Households in 2022” report, the U.S. Federal Reserve System Board of Governors found that many Americans are unprepared for retirement. Twenty-eight percent indicated that they have no retirement savings, and about 31% of those not yet retired felt that their retirement savings are on track. Among those who have self-directed retirement savings, about 63% admitted to feeling low levels of confidence in making retirement decisions. Low financial literacy has left millennials—the largest share of the American workforce—unprepared for a severe financial crisis, according to research by the TIAA Institute. Even among those who report having a high knowledge of personal finance, only 19% answered questions about fundamental financial concepts correctly.
A 2021 survey by the Federal Reserve Bank of San Francisco revealed that 28% of all payments were via credit card, with only 20% being made in cash. In India this is bound to be much more skewed in favour of digital payments, with the ubiquitous presence of UPI. Given high volume of online transactions and multiple banking products for individuals, there is a need for greater financial literacy to ensure every individual makes the most of her hard-earned money.
Financial Literacy can cover short-term as well as long-term financial strategies. The key is to simulate WHAT-IF scenarios of various investment decisions and visualise their impact across years and even decades, well in advance. Today, most of this occurs in MS Excel and is largely based on linear data projections or on a logarithmic scale. Can we visualize the consequences of our financial choices with the advent of technologies like AI and machine learning (ML) models? I believe so. To top it, consider it as a visualised, gamified scenario builder in Virtual Reality (VR), the visual layer of the Metaverse.
Role of Immersive Technologies
Imagine your financial investments playing out their profit-loss cycles across decades, thanks to AI modelling. These What-if scenarios would provide greater education and retention of one’s decision-making as far as financial instruments are concerned. As newer products enter the market, a constant training to these models will ensure the What-if scenarios remain invaluable for you as in individual investor. Taking it a step further and looking at visually gamifying the entire basics of financial literacy (Finance 101), it could prove to be a powerful learning tool for students in schools and colleges as well as working professionals.
Memory retention with VR is far greater than attending lectures, videos or e-learning modules. While the learning retention is only 5 percent for lectures and 10 percent for reading, we find VR among the top 2 with a learning retention of 75 percent. VR training is only beaten by learning that happens through educating others, where the learning retention is at 90 percent.
The learnability and application of knowledge would become second nature for every individual, thereby raising financial literacy, exponentially. There is a need to tap into the power of this technology for a crucial knowledge capsule that’s absent in the masses. This would ensure financial stability and growth in every individual beyond the cycles of survival and existence.
In conclusion there is a need to increase financial literacy in global population and immersive technologies like VR ably powered by AI could prove to be transformative in serving this need. Technology is the biggest leveller across urban and rural communities worldwide and hence could serve as a powerful tool ushering in this much needed aspect among various facets of literacy, financial or otherwise.
Whilst using manpower takes time and money, AI systems can scan masses of data sets, analyse data, spot anomalies, and therefore detect possible cyber risks before they have even happened.
What is 50/20/30 rule of financial planning?
50% of your monthly salary maximum should go on things you need to pay for: housing, bills, groceries & insurance. 30% can go on doing the things you enjoy: hobbies, drinks and travel. 20% should go into your savings: think about your long term savings and investment goals.
Most Seached Queries
Virtual Reality (VR)
General Data Protection Regulation (GDPR)
Payment Card Industry Data Security Standard (PCI DSS)
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I am Amar Kumar, pursuing BTech in chemical engineering from IIT Guwahati. Being a first-year engineering student and a tech enthusiast, I am in awe of the drastic ways technology has and continues to shape the future of engineering. Among the most revolutionary innovations, Artificial Intelligence stands out as a transformative force that is reshaping the engineering landscape. From optimizing processes to enhancing decision-making, AI has proven to be an invaluable model in the field of engineering.
In the engineering sector, design and simulation play a pivotal role in product development and problem-solving. AI-powered algorithms have significantly enhanced these processes, making them faster and more precise. AI can analyse large amounts of data to identify patterns, optimize designs, and stimulate real-world problems with accuracy.
With AI-driven generative designs, engineers can now explore countless design possibilities, leading to innovative solutions that were previously difficult to comprehend. This expedites the prototyping phase and ultimately reduces time-to-market for the products.
The introduction of automated systems, such as self-driving cars and unmanned aerial vehicles (UAVs), only became possible due to advancements in AI and machine learning. Engineers in the sectors of automotive and aerospace industries are at the forefront of developing such technologies.
By combining computer vision, sensor fusion, and decision-making algorithms, automated systems can navigate complex environments, adhere to traffic rules, and adapt to changing conditions. The potential benefits of these systems are vast, ranging from increased road safety to improved logistics and efficiency in transportation. One of the best example for this is the car Tesla of CEO Elon Musk.
AI’s influence has also extended into other sectors such as healthcare, overseeing a revolution in aspects like diagnosis, treatment, and patient care. AI algorithms can analyse large datasets to identify potential drug candidates and optimize treatment plans while AI-powered medical imaging has increased accuracy in identifying diseases, viruses, mutations and abnormalities.
In biomedical engineering, AI-driven simulations aid in designing medical devices and prosthetics, resulting in better patient outcomes and improved quality of life.
In an era of growing environmental concerns, engineers are tasked with finding sustainable solutions to pressing global challenges. AI has played a crucial role in fostering eco-friendly practices across industries. For instance, it has enabled smart energy grids that optimize energy distribution and consumption.
AI algorithms can predict energy demand patterns, allowing for efficient allocation of resources and reducing waste. Additionally, AI is used in environmental monitoring to track air and water quality, enabling early detection of pollution and facilitating remedial actions.
As a tech enthusiast and engineering student, I am incredibly excited about the role Artificial Intelligence will play in shaping the future of engineering. From transforming design processes to enabling automated systems and promoting sustainability, AI has opened a door to new possibilities and challenges for engineers across the globe.
As technology continues to advance, it is imperative for aspiring engineers to embrace AI as a powerful tool in their arsenal. By harnessing the potential of AI responsibly, engineers can drive innovation and create a more efficient, sustainable, and technologically advanced world for generations to come.
AI is used in environmental monitoring to track air and water quality, enabling early detection of pollution and facilitating remedial actions.
How AI supports the field of biomedical engineering?
In biomedical engineering, AI-driven simulations aid in designing medical devices and prosthetics, resulting in better patient outcomes and improved quality of life.
Most searched queries
Unmanned Aerial Vehicles (UAVs)
Computer vision
Sensor fusion
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As marketers, we fundamentally learn about the 4 Ps of Marketing: Product, Price, Place, and Promotion. Later, three more Ps were added: People, Process, and Physical evidence. Whatever we do revolves around the customer.
AI will disrupt all the Ps in many ways going forward, and many times there is an argument: will that take jobs? My assumption is that it will take non-productive jobs and give space to marketers to work on something newer, something smarter.
This is the role of technology! To assist humans in making the best possible product, but never forgetting that “human touch”. Marketing is all about creativity, empathy, and understanding human behavior. Successful marketing needs originality and a creative spark only humans can possess.
Having said that, a 2020 Deloitte global survey of early AI adopters showed that three of the top five AI objectives were marketing-oriented: enhancing existing products and services, creating new products and services, and enhancing relationships with customers.
The main job of any marketer is understanding the need of the customer, matching it with the offerings like product and services, and persuading the customer to buy the product and service.
This looks simply, a 3-step process, but there are a lot of critical steps & information involved in-between where marketers must analyze a lot of data and tweak the marketing strategy accordingly.
Let’s discuss it here with some real-time examples and see how marketers are using AI to support them.
1.Content Marketing –
Currently, AI can help in customizing the marketing content like product information, email writing, blogs, marketing messages, and copywriting. All this can be done using open AI tools available and should work on simple prompts.
With machine learning, this data can be used further for creating sales pitch, cross-selling pitch, customer engagement, last-minute deal or offer by considering different variables like demographics of the target consumer, behavior, demographic, along with deep analysis of the impact of communication. Example AI tools.
2. Data Analytics –
Slicing and dicing of data was done earlier as well, but AI has taken it to the next level where one can get predictive analysis and prompts/suggestions to enhance the content/marketing campaign.
Customer data like preferences, engagement, status as in which ladder of purchase funnel the customer and CRM, and that gives space to marketers for redefining the market strategy. Example CRM tools.
3. Search Engine Optimization –
This has been a game-changer with real-time examples like Google, Netflix, and other search engines. This will help segmentation of customers and suggest target advertising. This has also helped marketers to leverage cross-functional platforms.
So, if you search for some product on Amazon and open any social media like Instagram or Facebook, you will see recommendations of the same or similar products on that platform as well. By doing this, marketers can enhance the recall value of products or services.
4. Placement of advertisement –
This has been very important: where to post an advertisement for the best ROI. AI has made it easy to target or place advertisements based on consumer data like purchase history, preference, and context of purchase.
Example Google/YouTube advertisement.
5. E-commerce and Digital Marketing –
AI has been widely used by e-commerce websites and digital marketing to reach out to the right customers, understand their needs and buying patterns, and automate marketing workflows and course correction of marketing efforts which otherwise would have taken a lot of resources. Example any AI-enabled fitness app.
AI can be a game-changer in many ways, but Human decision-making is typically reserved for the most consequential questions, such as whether to continue a campaign or to approve expensive TV ads.
Training your algorithm enough that it should give expected results, training algorithms with correct prompts, and above all, safety and security of data.
Data privacy is going to be of utmost importance for AI. Clear and transparent policies need to be drafted against data security and privacy.
I believe AI is like a child which needs to be trained to become a responsible assistant; hence humans have a greater role as to how they are raising this child to serve mankind.
As marketers continue to embrace AI technologies, they must strike a balance between innovation and ethical considerations, leveraging AI’s capabilities to enhance customer experiences while upholding trust and transparency.
4 P’s of Marketing: Product, Price, Place, and Promotion. Later, three more P’s were added: People, Process, and Physical evidence.
What is the key to successful marketing?
Successful marketing needs originality and a creative spark.
How is machine learning serving the marketing industry?
With machine learning, data is used for creating sales pitches, cross-selling pitches, customer engagements, last-minute deals, or offers along with deep analysis of the impact of communication.
Most searched queries
Machine learning
CRM (Customer Relationship Management)
SEO (Search Engine Optimization)
ROI (Return on Investment)
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We stand at the crest of a tidal wave of technological disruption. Artificial intelligence is automating tasks that have consumed our productive hours for decades: data entry, analysis, manufacturing, and creative work. AI systems can now shoulder the repetitive drudgery, freeing our time and consciousness for higher pursuits.
Leading companies have already embraced this automation revolution. At JPMorgan Chase, AI models now handle mundane tasks like data entry and document processing, saving the bank 360,000 hours of work annually. Pharmaceutical giant Novartis uses AI to crunch massive datasets and rapidly identify promising new drug candidates based on molecular interactions.
Embracing Our Humanity
Rather than viewing AI as a threat, we should welcome it as a liberating force to help us reclaim our humanity. By offloading rote labor to intelligent machines, we can redirect our energy toward the uniquely human capacities that spark innovation, deeper connection, and meaning.
An Abundance of Possibility
AI is gifting us a precious new resource: time. Imagine an extra 20–30 hours per week because tedious tasks were automated by intelligent systems. How might you spend that liberated time?
You could finally nurture creative passions—painting, music, and writing. Be truly present with loved ones on adventures over heartfelt conversations. Prioritize nourishing self-care like exercise, cooking nourishing meals, and mindfulness practices.
AI handles the draining, obligatory labor that consumes so many hours. In doing so, it provides the ultimate modern luxury: abundant time to experience the simple joys that light up our human spirits. We regain the spaciousness to live each day with more intention, purpose, and richness.
The automation revolution doesn’t just optimize productivity. It alchemizes our most limited resource into an abundance of what makes us feel vibrantly alive. How will you spend this reclaimed gift of time?
Catalyzing a New Renaissance
Some fear AI will make us lazy, unskilled, and purposeless. However, unburdened from menial, robotic toil, we’re empowered to cultivate the loftier capacities that only we possess: abstract reasoning, emotional intelligence, and radical ingenuity. AI could catalyze an explosive renaissance across the spheres of human inquiry, creation, and growth.
Consider how Pfizer adopted natural language processing AI to scan millions of genetic databases and academic papers, accelerating the identification of a potential drug target for COVID-19. Augmented by AI’s analytical scale, human scientists and physicians can rapidly unearth insights that dramatically accelerate innovation.
Uplifting Society’s Masses
Most transformative, AI-driven production efficiencies and abundance can economically uplift millions globally from the cycle of empty labor performed solely for survival. By making the essentials of comfortable living affordable for all through advanced AI systems, we expand access to quality education, creative leisure, and self-actualization for humanity’s masses—key drivers of cooperation, societal flourishing, and progress.
Human Skills for the AI Age
But to truly thrive in this era, we must cultivate the vital human skills that intelligent machines cannot yet replicate: creativity, emotional intelligence, adaptability, grit, and a sense of higher purpose.
As “The Happy Doctor,” I’ve spent 28 years helping thousands of professionals across six continents unlock these capacities through science-backed strategies blended with spiritual wisdom and transformative frameworks. Top companies like AC Delco, Continental, Qualcomm,Fitness First, Commonwealth Bank & Trust Company, ITC Hotels, Famous Amos, and Kawasaki covet my guidance to inspire thriving, engaged, innovative cultures amidst the uncertainties of technological upheaval.
Rekindling Human-Centered Living
Ultimately, the rise of AI beckons a revival of human-centric living. As intelligent systems take on ever more obligatory labor, we gain the freedom to mindfully sculpt our days and energies with greater intention. We rekindle imagination, emotional depth, and purposeful presence—the wellsprings of rich, vibrant, and meaningful living. AI is not a threat, but an invitation to reconnect with the essence of our humanity.
The future made possible by AI is wondrous if we have the wisdom to embrace its liberating possibilities. Let’s harness this powerful technology not just for the sake of automating efficiencies but as a catalyst to unleash the highest, brightest, and most joyful expression of our human potential.
A testimonial for my work I would like to share: Happiness Hero Sparking: A Global Awakening
One conversation with Suresh and his vibe is infectious—you’ll see why he’s called “The Happy Doctor.” This pioneering thought leader blends spiritual wisdom, science, and transformative methods to awaken human excellence amidst technological change. Suresh has ignited tens of thousands across six continents, proving happiness is an essential skill we can cultivate. His work unlocks creativity, resilience, and boundless potential—key qualities companies need to thrive.
Most asked questions
How is AI helping the banks?
At JPMorgan Chase, AI models now handle mundane tasks like data entry and document processing, saving the bank 360,000 hours of work annually.
How much time does AI help us to save weekly?
AI can help us save 20-30 hours per week and gifts us a beautiful opportunity to upskill ourselves.
What is the purpose of Pfizer?
Pfizer is an American multinational pharmaceutical company. It also took the initiative to produce vaccines against the COVID-19 pandemic.
Who is known as “The Happy Doctor” and why?
Dr. Suresh Devnani is called “The Happy Doctor.” He is a tech thought leader who blends spiritual wisdom, science, and transformative methods to awaken human excellence amidst technological change.
Most searched queries
Novartis
Pfizer
Artificial Intelligence
Emotional Intelligence
Automation revolution
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I am Sharnaya Panag, a graduate of O. P. Jindal Global University with a Journalism and Communication degree. I am fascinated by the hold machine and IoT has on our species, knowing that what has become of us, cannot be undone. My greatest interest is understanding and writing about our Psyche and the future we will create for ourselves.
Can mankind’s unquenchable thirst to make life easier lead to our own downfall?
It was around 5 million years ago that for the first time, the planet witnessed the breakthrough of sentient life. Nature created a species so astute, one that would never act on behalf of the will of the planet, but the will of itself.
Since Man’s inception, there has been one thing that has remained constant throughout our history. If the past has shown us one thing it is that humanity will go to great lengths to create a life of increasing comfort.
We have a very deep connection with our past, as our ancestors left behind a trail of evidence to help us decode our history. The more we read, the more it helps us to communicate with the past.
One can take a look through manifold scriptures, some pieces of literature or even just a page out of a diary, and for a moment the reader gets to delve into the mind of somebody who lived ages before they did.
This highlights the significance of reportage, why is it so important for us to report? Not just for the present, to spread awareness, but also to leave proper documentation for the future to analyse, just as we have been doing with documents of the past, trying to connect the dots of history.
Over the course of our time, man has remained invested in breaking through to new technological advances to make everyday life easier.
Machine was created to cut down on physical labour and for the first time, the world saw the mass production of goods in rapid time.
Fast forward to 2023, and we are now sitting in a world entirely supported by machine. The time of self-sufficiency is over. In our society, our lives wholly revolve around them for the sake of our comfort.
Phones, laptops, cars, aeroplanes, trains, ATMs, air conditioners, and heaters, are just a few appliances we use on a daily basis, without which the lives of many would crumble.
However, the invention that completely altered the course of our evolution would be the invention of the internet. 30 years ago, on the 30th of April, 1993, the World Wide Web was made accessible to the public.
It crept its way to every nook and cranny it could and now trillion gigabytes of data are in the hands of each and every human being that can afford to access it. The overall pace picked up by humanity seemed to be quicker than ever before.
This, of course, was also not enough. Mankind is always willing to test its limits. Hence came the next chapter, the creation of an artificial body, an entity in itself. The creation of machine to decrease physical labour felt inadequate.
Man wanted life to be effortless, we consider ourselves so superior, that we refuse to even think for ourselves, therefore the creation of Artificial Intelligence. The most popular example of this would be Chat GPT, launched on the 30th of November, 2022, an AI created to help people structure their thoughts and opinions into words not written by themselves.
Artificial Intelligence is a body that has been created to think for us. To research data, store it, assimilate it, paraphrase it, and structure it in detail in the form required by the user. Chat GPT uses web scraping as a tool using automated methods to scan websites, retrieve data and synthesize it to provide the user with a well-grounded assimilation of it.
It also has a plug-in feature which allows it to interconnect with third-party websites. Chat GPT uses the Bing API which permits two or more computer programs to communicate and uses it to navigate through the web to gather data. It can cite every source so that the user has access to every website it went through.
Students are now using Chat GPT to write their assignments, and even people working in multinational corporations use it to assimilate data and statistics. The need has become so severe that people are now using it to write mail since formatting an e-mail has become such a task.
We are dealing with an entity beyond our understanding. A trillion gigabytes of data, endless information at the fingertips of this AI. The majesty of the human brain is undeniable, but can the brain possibly compete with an AI whose brain we can potentially attribute to the entire internet?
There is nothing stopping companies from permanently resorting to using AI to achieve profitability. The wheel is already set in motion, just in the month of July of 2022, the founder and CEO of e-commerce firm Dukaan, Suumit Shah took to X(formerly called Twitter) to announce that 90% of the customer crew of his company has been replaced by an AI chatbot.
He added that the response time has dropped from a whopping two hours to just three minutes and the cost related to customer care was brought down by 85%.
Being sentient beings, our species has evolved beyond belief. We have the privilege to read or write anything we desire in detail, and we can preserve our beliefs for future generations. The world today is obsessed with taking the easy way out because of how fast-paced society has become.
In the past writers, and documentarians from all over the globe went to great lengths to collect, assimilate, and structure data. People spent years trying to extract the information necessary, going through extensive bodies of work, scriptures, research papers, articles, books, speeches, and various other documents.
They did this all to keep the populace aware of world events, to provide them with entertainment or even just writing for fun.
At the centre of it all remains effort, and creativity. However, now there is an entity that can produce material in a matter of seconds, in turn reducing the effort we put in to educate ourselves, leading to the inevitable culmination of creativity.
Without creativity, our credibility to commit to the task will be put to the test. Our skills may fall short in the face of Artificial Intelligence. Looking at journalism and writing as an example, will man be able to make the effort and hold on to creativity?
The problem is that if our mind isn’t put to work, and is given constant chances to escape effort, it will lead to the impotency of the human brain. If the students of today use interfaces as a loophole to avoid the task at hand, the pattern will spread and over the course of time, it is bound to become a habit.
In the near future, resorting to Artificial Intelligence will become the norm, just as relying on machinery, technology, and the internet became the social norm.
The facts are displayed in front of us very clearly. Humanity must be able to spot the pattern otherwise all we can do is sit back and watch our own downfall. Companies will always vouch for their profitability and benefit. It will be difficult to hold on to reality as we know it.
There will be no jobs left for man if we accept and allow AI to become a part of our lives and the social norm. The progress of Artificial intelligence must be put to a stop or at least slowed for the preservation of the future of upcoming generations.
When was the World Wide Web made accessible to the public?
How AI chatbots are profiting companies and businesses?
How AI will lead to the decline of human aptitude?
Most searched queries
Bing API
ChatGPT
Artificial Intelligence
Gigabytes
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Imagine a world where YOU are surrounded in as science fiction surrounding with supportive robotics helping you everywhere 🌐. Also, imagine robots that can cook your favorite meal and can perform surgery with super pinpoint accuracy – the future is here, and it will be automated in so many fields 🤖
Let’s dive into some of the latest developments in robotics and take a peek into what the future holds for us 🔍.
Personal assistant robots:
Gone are the days when robots were confined to only factories. Today, personal assistant robots are making their way into our homes and hospitals, helping with tasks ranging from cleaning to even keeping us company 🏠.
For example, robots like “RoboChef,” which can whip up a gourmet meal with the ingredients you have at home. Imagine coming home to the smell of freshly cooked pasta without lifting a finger and being greeted with kind courtesy 🍝.
Medical precise assistants:
In the medical field, robotics technology is nothing short of being revolutionary 🏥. Surgical robots, such as the “DaVinci,” allow doctors to perform very complex surgeries with more precision and smaller cuts ✂️.
This means a far quicker recovery and less risk of infection for patients. AND there’s ongoing research into robotic prosthetics that can be controlled by the mind as just recently successfully tested, promising a new level of independence for amputees 👤.
Industrial giants:
Industrial robots are getting smarter and more flexible 🏭. They’re no longer just about heavy lifting or repetitive tasks. Now, they can adapt to different tasks, learn from their environment with AI, and work safely alongside humans.
This flexibility is revolutionizing manufacturing, making it more efficient and less vulnerable to errors 🛠.
Service robots are not expecting anything in return aside from energy:
Service robots are popping up in various sectors, from hospitality to retail. In some restaurants, robotic waiters nowadays already serve food, offering a unique at least different dining experience 🍽.
Meanwhile, in retail, robots are being used to manage inventory, freeing up human workers for more complex tasks. This not only improves efficiency but also enhances the customer experience in many aspects 🛒.
Our environmental warriors that “risk” their lives:
Robots are also playing a crucial role in environmental conservation 🌍. For instance, underwater drones are being used to monitor ocean health and track marine life 🐠.
On land, robots are engaging to clean up waste and even plant trees. These robotic environmental warriors are essential tools in our fight against climate change 🌱.
Fluid robotics: Shaping the flow of innovation:
A lesser-known yet fascinating frontier in robotics is the development of fluid robots and this kind of reminds me of the movie Terminator, where you have a fluid robot.
Even though we are not that far, robots are designed to move and operate within liquid environments, not just by swimming or floating but by becoming part of the fluid itself 💧.
Imagine tiny robots that can change their shape to flow through the narrowest of pipes or veins, making them perfect for tasks ranging from repairing underground water pipes without burdensome excavation as well as delivering targeted medicine within our human bodies 💊.
Nano robotics: The tiny titans of technology:
Nano robotics takes the concept of robotics to an incredibly small scale, operating at the level of atoms and molecules! These microscopic robots named nanobots, have the potential to revolutionize many fields, from medicine to materials science 🔬.
For example, in medicine, nanobots will be inserted to attack cancer cells with revolutionary precision, repair damaged tissues far more precisely, or check vital signs from within the body 🧬.
But also beyond healthcare, nano robotics will enable the developments of new materials with extraordinary properties, such as self-healing surfaces or ultra-lightweight and super-strong structures.
Labor shortages and are often solved by robotics:
In addition, as global labor shortages becoming more relevant, robots are stepping in to fill in the gap, taking on roles that are really boring, dirty, and especially dangerous 🚀!
Robots are being developed for a wide range of tasks, from preparing meals to doing laboratory tests as well as showing their efficiencies and potential.
Looking towards bright next years: A robotics renaissance!
As we look towards 2024, I find the robotics industry is positioned right now for a renaissance with more startups and scaleups really pushing innovations further.
From autonomous material movement and collaborative robots (Cobots) to the integration of AI, IoT, and cloud computing, the next wave of robotics is ready now to be farmore interconnected, intelligent, and efficient than ever before 🌟.
Future Outlook:
Looking ahead, the possibilities are endless 🌟. I believe we’re on the verge of seeing self-repairing robots 🤖 that can diagnose and fix themselves, eliminating huge downtimes.
Another exciting aspect is the development of emotion-sensing robots ❤️, that can respond to human emotions, making them even more integrated into our daily lives.
One can also expect the rise of more sophisticated AI in robotics 🧠🤖, leading to machines that can make decisions and solve problems by themselves.
This will revolutionize industries like disaster response 🚨, where robots can be sent into dangerous areas to provide help without putting human lives at risk.
In Conclusion:
Robotics is changing our world rapidly, making everyday tasks simpler and more efficient. They’re even addressing big issues like healthcare and environmental protection 🌳.
As we move forward, robots will become an even bigger part of our lives, opening up exciting possibilities.
Not without reason did 🚀 Elon Musk predict 1 billion humanoid robots by the year 2040. For sure is, they’re already now a reality, not just a future prediction, and they’re here to stay and get better week by week 💡💛.
Most asked questions
What is the purpose of using robots in every sector?
How is nano-robotics helping to combat the cancer?
How are robotic warriors fighting against climate change?
Most searched queries
Humanoid
Nano robotics
Fluid robotics
DaVinci
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I am a 2nd year student at Nanyang Technological University. The inspiration for this research article is the rapid and advanced development of AI and CRISPR technology in the medical field. AI’s rapid data analysis transforms drug development, while CRISPR precision revolutionizes genetic modification. Together, these two incredible technologies result in groundbreaking developments, shaping the future of healthcare with innovative treatments and personalized medicine.
Read my article below:
Section 1: CRISPR Technology
CRISPR technology is derived from bacterial immune systems to facilitate precise gene modification. It is made up of short repetitive DNA sequences that contain “spacer” sequences, which contain viral genetic information.
By utilizing Cas9 enzyme as molecular scissors, RNA molecules can guide CRISPR to target and edits specific genes precisely. With this capability, scientists can introduce new genes or modify existing sequences accurately.
CRISPR has a profound impact on treating genetic disease, because it can modify faulty genes that are responsible for these hereditary conditions. Therefore, CRISPR can treat diseases that cannot be treated through therapeutic interventions.
This makes CRISPR a revolutionary tool in the pursuit of precise and effective treatments for many genetic diseases. This is due to its precision, versatility, and transformative impact.
A group of scientists from China has demonstrated that CRISPR can eliminate or inactivate carcinogenic viral infection (cancers are caused by gene mutations). They have proven that CRISPR can treat cancer when it is applied to human viruses, such as hepatitis B virus and HPV16.
For example, HPV16 and HPV18 viruses can induce cervical cancers by Papillomavirus E6 and E7 viral proteins. Bacterial CRISPR/Cas RNA guided endonuclease can be reprogrammed to HPV-transformed cells to knockout (delete) E6 and E7 genes.
One of the clinical challenges faced is the off-target effects even though CRISPR can edit and modify gene precisely. Many researchers have reported that the CRISPR-Cas9 technology cause gene modification in other undesired genomic loci. As a result, this will reduce the efficacy of gene modification.
To reduce the off-target effects of CRISPR-Cas9, a scientist from Harvard university has modified the Cas9 protein to enhance the recognition of target DNA. Hence, it can improve the on-target specificity and efficiency of CRISPR-Cas9 technology.
Section 2: AI accelerates drug development
The advanced algorithms of artificial intelligence (AI) can revolutionize the development of drug by analyzing extensive dataset with speed and accuracy.
These algorithms can identify potential drug candidates more efficiently than traditional methods. Due to its ability to identify intricate pattern and relationships within the extensive and diverse data, it can lead to more informed decision-making.
The complex algorithm in AI can also reduce the time and resources required for early-stage research. Hence, this innovative application of AI marks a paradigm shift, creating the hope of streamlining drug discovery to bring novel and effective treatments to patients more swiftly.
AI has emerged as a possible solution to the problems caused by chemical space of atoms in the pharmaceutical industry.
The AI algorithms have been increased in computer-aided drug design (CADD) due to the development of technologies and high-performance computer.
The two most common methods of CADD are structure-based drug design and ligand-based drug design. The structure-based drug design analyzes the three-dimensional of proteins, while the ligand-based CADD uses the information of studied active and inactive molecules.
Machine learning computational algorithms, such as support vector machine (SVM), has ensured to improve the activity of bioactive components.
The combined methods of both deep-learning and machine-learning has increased the ability, strength, and standard of the evolved products.
In the field of orthopedics, the large amount of data with the inclusion of ML has helped orthopedic surgeons in many aspects of the application.
For example, the advances in this field to assess the impact on the musculoskeletal system of human beings. This is done to provide value-based healthcare and serving the patients in a better manner.
Section 3: Synergy of CRISPR and AI
The integration of CRISPR and AI has led to a new era of unprecedented advancements in the development of drug discovery.
As a result, many critical challenges can be addressed. There will be more novel solutions and the pace of scientific breakthroughs will be accelerated.
The synergy of CRISPR and AI potential drug targets to be identified rapidly in the process of drug discovery and the assessment of their therapeutic viability.
It would be impossible for human researchers to decipher the massive genomic information. AI algorithms make it possible due to its ability to analyze vast data sets, identifying patterns and relationships.
Furthermore, AI has an important role in optimizing the process of predicting the outcomes of genetic editing in CRISPR experiments.
This is because the algorithms can anticipate the effects of specific gene edits after studying the previous CRISPR data. In this way, it can learn from past experiments.
The ability of AI to predict results in experiments not only speeds up the experiment, but also reduce risks to increase accuracy. Therefore, the synergy of CRISPR and AI can revolutionize the landscape of biotechnology.
For example, machine-learning (ML) models are trained using existing datasets and can be used to predict the on/off-target effects of the testing datasets (genomic information).
The current ML models are based on regression-based methods, classification-based methods, and ensemble-based methods.
The advanced ML models enable deep-learning (DL) methods to be applied in the CRISPR-Cas9 system. The models in CIRSPR-Cas9 system consists of multiple layers of interconnected compute units.
The algorithm takes the encoded gRNA-DNA sequence in length 23 in the matrix as input. The convolution layer applies various filters of different sizes to the input matrix.
The next layer performs batch normalization to the output of convolution layer to boost learning and prevent over-fitting.
The last layer (pooling layer) further filters the normalized data from the previous layer. The output of this layer is then passed through multiple layers of deep learning neural network.
The last layer of this network passes the result to the stop layer that will predict whether the input is off-target or on-target.
Conclusion
In conclusion, the combination of CRISPR and AI has led to a revolutionary era in biotechnology. The coupling of the precision of CRISPR in genetic modification and AI in drug development has resulted in a groundbreaking development in drug discovery.
This showcases the transformative potential of this dynamic collaboration.
Can AI be more efficient than traditional methods in predicting diseases?
How AI can be used to decipher DNA?
Most searched queries
Machine learning
Genome
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