#note: this project is a traditional generative algorithm not AI

[deramin2]

The world is scary and isolating and overwhelming. It's hard to notice all the little ways people help to make positive collective change. But there's an easy way to go hear some of it at any time:

Listen To Wikipedia by Hatnote

Listen to Wikipedia is a generative ambient musical and visual art piece that's been running since July of 2013 based on Wikipedia edits. Considered as a specific song, it has been playing continuously for over a decade.

"Bells are additions, strings are subtractions. There’s something reassuring about knowing that every user makes a noise, every edit has a voice in the roar. (Green circles are anonymous edits and purple circles are bots. White circles are brought to you by Registered Users Like You.)"

Enough people are out there trying to share some understanding of the entire world around us at a fast enough that in can make interesting music. We're not doing this alone. Many hands make lighter work. Wikipedia is a wonder of the modern world because millions of people willed it into existence together, and the act of that creation constantly continues.

[aionlinemoney]

India’s Tech Sector to Create 1.2 Lakh AI Job Vacancies in Two Years

India’s technology sector is set to experience a hiring boom with job vacancies for artificial intelligence (AI) roles projected to reach 1.2 lakh over the next two years. As the demand for AI latest technology increases across industries, companies are rapidly adopting advanced tools to stay competitive. These new roles will span across tech services, Global Capability Centres (GCCs), pure-play AI and analytics firms, startups, and product companies.

Following a slowdown in tech hiring, the focus is shifting toward the development of AI. Market analysts estimate that Indian companies are moving beyond Proof of Concept (PoC) and deploying large-scale AI systems, generating high demand for roles such as AI researchers, product managers, and data application specialists. “We foresee about 120,000 to 150,000 AI-related job vacancies emerging as Indian IT services ramp up AI applications,” noted Gaurav Vasu, CEO of UnearthInsight.

India currently has 4 lakh AI professionals, but the gap between demand and supply is widening, with job requirements expected to reach 6 lakh soon. By 2026, experts predict the number of AI specialists required will hit 1 million, reflecting the deep integration of AI latest technology into industries like healthcare, e-commerce, and manufacturing.

The transition to AI-driven operations is also altering the nature of job vacancies. Unlike traditional software engineering roles, artificial intelligence positions focus on advanced algorithms, automation, and machine learning. Companies are recruiting experts in fields like deep learning, robotics, and natural language processing to meet the growing demand for innovative AI solutions. The development of AI has led to the rise of specialised roles such as Machine Learning Engineers, Data Scientists, and Prompt Engineers.

Krishna Vij, Vice President of TeamLease Digital, remarked that new AI roles are evolving across industries as AI latest technology becomes an essential tool for product development, operations, and consulting. “We expect close to 120,000 new job vacancies in AI across different sectors like finance, healthcare, and autonomous systems,” he said.

AI professionals also enjoy higher compensation compared to their traditional tech counterparts. Around 80% of AI-related job vacancies offer premium salaries, with packages 40%-80% higher due to the limited pool of trained talent. “The low availability of experienced AI professionals ensures that artificial intelligence roles will command attractive pay for the next 2-3 years,” noted Krishna Gautam, Business Head of Xpheno.

Candidates aiming for AI roles need to master key competencies. Proficiency in programming languages like Python, R, Java, or C++ is essential, along with knowledge of AI latest technology such as large language models (LLMs). Expertise in statistics, machine learning algorithms, and cloud computing platforms adds value to applicants. As companies adopt AI latest technology across domains, candidates with critical thinking and  AI adaptability will stay ahead so it is important to learn and stay updated with AI informative blogs & news.

Although companies are prioritising experienced professionals for mid-to-senior roles, entry-level job vacancies are also rising, driven by the increased use of AI in enterprises. Bootcamps, certifications, and academic programs are helping freshers gain the skills required for artificial intelligence roles. As AI development progresses, entry-level roles are expected to expand in the near future. AI is reshaping the industries providing automation & the techniques to save time , to increase work efficiency. 

India’s tech sector is entering a transformative phase, with a surge in job vacancies linked to AI latest technology adoption. The next two years will witness fierce competition for AI talent, reshaping hiring trends across industries and unlocking new growth opportunities in artificial intelligence. Both startups and established companies are racing to secure talent, fostering a dynamic landscape where artificial intelligence expertise will be help in innovation and growth. AI will help organizations and businesses to actively participate in new trends.

#aionlinemoney.com

[kolajmag]

There is a new issue of Kolaj Magazine!

Kolaj is a quarterly, printed, 10"x8" art magazine featuring reviews and surveys of contemporary collage. The magazine takes an international perspective on collage as a medium, a genre, a community, and a 21st century art movement.

SUBSCRIBE OR ORDER A COPY

INSIDE THE ISSUE

In On attrape bien les followers avec des likes (We Are Good at Catching Followers with Likes), this issue’s cover image, a tiny man watches a boy who watches a man holding a sign with an Instagram like on it. Two women gaze from a distance. A sign in the back says, “Believe Me”. The collage is a commentary on how social media fights for our attention, devotion, and patronage. A portfolio of SLip‘s collage appears in the issue. 

In the issue editorial, Ric Kasini Kadour asks what happens when a work of art becomes detached from the artist, orphaned, unidentified? “Lost Collages” considers those artworks found in attics and thrift shops, divorced from their maker.

In the issue’s round up of News & Notes, we report on Canadian collage artist Rhonda Barrett who opened Cuts & Paste Gallery in Halifax, Nova Scotia in January 2023 and Alicia Saadi‘s billboard installation in New Brunswick, New Jersey. (Saadi recently took part in Kolaj Institute’s Collage as Street Art Residency.)

In “The Abbondanza of Order & Chaos,” Austin, Texas collage artist Lance Letscher shares his idea of good art: “Not to fall into a cliché, but if the work transcends itself, it becomes something bigger or more complex, or more intricate, or more interesting or more curiosity provoking, then it’s more than the sum of its parts: it’s a successful piece of artwork.” Check out or interview with this legend. 

“I came to poetry by accident…Which brought me to collage,” writes Kim Triedman in “The Poetics of Collage.” The Massachusetts artist and award-winning author recalls her journey and how poetry and collage mingle in her practice. 

Anna Rémuzon explores the subtle, vibrant meeting of Expressionism, Surrealism & Pop Culture in the artwork of Czech artist Tomáš Jetela in “A Rare Hallucination of Reality…Defragmented.” Rémuzon writes, “The characters are fragmented, detached pieces. The faces, the articulated bodies, the accessories—each element has its own origin, its own identity. Jetela ‘glues’ them together in his composition, which creates a new, mutant form of humanity.”

Steven Specht, Ph.D., NCS picks up on Mark Vargo’s article about AI art that appeared in the previous issue in “Art, artifacts, & authenticity: The Problem of De-coupling Process from Product.” He writes, “As a medium, collage lends itself to being mimicked by computer generative algorithms. But I would argue that any generative AI process represents nothing close to the authentic experiences that Kyle [Riecker], or I, or any other collage artist experiences in the process of creating art—even if the process itself is chaotic.”

“The Art of Joy” reports on The University of Colorado Collecting of Frances Joy Bradbury’s COVID-19 project. The artist worked with curator Tyler Alpern, a longtime artist friend, to turn the collection into a book which presents a sample of the collages the artist made every day during the COVID-19 pandemic in 2020 and 2021. 

Evelyn Rapin shows us ekphrasis in reverse in “The Secret Life of Plants.” She writes, “Experimentation, chance and accident are huge elements to be embraced in collage assembly.”

In “Southern Axis,” Ric Kasini Kadour writes, “History is always approached on an axis, a through line that guides the narrative. Art History is no different. Traditionally, art history is viewed through an academic lens looking at the evolution of painting and sculpture. A premise of Kolaj Magazine and Institute is that when we turn the axis of art history to collage, the traditional canon opens up to greater diversity.” Kadour reviews the 20th Anniversary Exhibition at the Ogden Museum of Southern Art with an eye to how collage challenges our view of the art history canon.

SUBSCRIBE OR ORDER A COPY

Kolaj 38 is sent automatically to members of the Silver Scissors & Golden Glue Societies.

[christianbale121]

How Is AI Copilot Development Transforming Productivity Tools?

In an age where speed, efficiency, and innovation define business success, productivity tools have evolved from simple task managers into intelligent systems that can think, analyze, and even assist in decision-making. At the heart of this transformation lies AI Copilot Development—an emerging technology that is revolutionizing how we work, create, and collaborate.

What Is AI Copilot Development?

AI Copilot Development involves the creation of smart digital assistants that actively support users in real-time. These AI copilots are integrated within productivity platforms—like word processors, spreadsheets, coding environments, and project management tools—to provide intelligent suggestions, automate routine tasks, and enhance user experience.

They leverage natural language processing (NLP), machine learning, and large language models (LLMs) to understand human input, respond contextually, and even generate content or code based on intent. Think of them as always-on collaborators who understand your workflow and help you complete tasks faster and smarter.

Transforming Traditional Productivity Tools

Productivity tools are no longer passive software; with AI copilots embedded, they are becoming interactive and adaptive systems. Here’s how:

1. Automating Repetitive Tasks

AI copilots can take over mundane tasks like scheduling meetings, formatting documents, generating summaries, and even sending emails. This automation frees up time for users to focus on strategic or creative work, significantly boosting productivity.

2. Enhancing Content Creation

In tools like Microsoft Word or Google Docs, AI copilots suggest real-time grammar corrections, tone adjustments, and even draft entire paragraphs or emails. For marketers and writers, this speeds up the content creation process while maintaining quality and consistency.

3. Boosting Coding Efficiency

For developers, AI copilots integrated into IDEs (Integrated Development Environments) can auto-complete code, detect bugs, and recommend better coding patterns. Tools like GitHub Copilot are already revolutionizing software development by cutting coding time and reducing errors.

4. Smarter Data Analysis

In spreadsheet tools like Excel or Google Sheets, AI copilots assist in creating complex formulas, visualizing data, and drawing insights automatically. Users can ask natural language questions like, “What were our highest sales months?” and get instant, data-driven answers.

5. Real-Time Collaboration

AI copilots enhance team collaboration by offering shared insights, tracking project progress, and summarizing discussions or meeting notes. They act as a central knowledge base that team members can rely on to stay aligned and informed.

The Impact on Work Culture

AI copilots are also transforming work culture by enabling a more empowered and agile workforce. Professionals no longer need to be experts in every tool—they can rely on AI to guide them through complex tasks. This levels the playing field and encourages cross-functional collaboration.

Moreover, the stress associated with manual multitasking and data overload is significantly reduced. AI copilots manage context, handle repetitive input, and even learn from your habits, leading to a more intuitive and personalized work environment.

Challenges and Considerations

Despite the benefits, AI copilot development comes with challenges:

Data Privacy: AI copilots need access to user data to function optimally, which raises concerns about security and compliance.

Over-reliance on Automation: There’s a risk of users becoming too dependent on AI, potentially undermining critical thinking and creativity.

Bias and Accuracy: AI models must be trained responsibly to avoid spreading misinformation or making biased decisions.

These concerns highlight the importance of ethical development, transparent algorithms, and human oversight in deploying AI copilots.

Looking Ahead: The Future of Productivity

The integration of AI copilots into productivity tools is just beginning. Future developments will likely include even more contextual understanding, emotional intelligence, and cross-platform synchronization. As AI continues to evolve, we’ll see copilots that can not only assist but also anticipate user needs, provide strategic advice, and evolve alongside users’ workflows.

Companies investing in AI Copilot Development today are positioning themselves at the forefront of a workplace revolution. These intelligent assistants are not just tools—they're becoming team members that drive efficiency, creativity, and innovation.

Conclusion

AI Copilot Development is redefining the capabilities of productivity tools. By embedding intelligence directly into the software we use daily, it’s turning static platforms into dynamic, responsive, and personalized work environments. As we navigate this transformation, the synergy between human creativity and machine intelligence will shape a more productive, empowered, and innovative future of work.

[onlineeducation1]

Online MCA in India: Eligibility, Fees, and Career Scope Explained

In today’s tech-driven world, a Master of Computer Applications (MCA) has become one of the most sought-after postgraduate degrees in India. With the rise of digital learning, Online MCA programs are gaining massive popularity, offering flexibility and accessibility to working professionals and fresh graduates alike. But what exactly does an Online MCA in India involve? Let’s break it down — from eligibility and fees to career scope.

What is an Online MCA?

An Online MCA (Master of Computer Applications) is a 2-year postgraduate degree designed to build advanced knowledge in computer science, software development, programming, and IT project management — all delivered through online platforms. These programs are UGC-DEB approved and offer the same academic value as traditional classroom degrees, with added flexibility.

Eligibility Criteria for Online MCA in India

To apply for an Online MCA program in India, you typically need:

A Bachelor's degree (preferably BCA, B.Sc. in Computer Science, or a related field)

Minimum aggregate marks: Usually 45–50% (varies by university)

Mathematics at 10+2 or graduation level (a common requirement)

Some universities may offer bridge courses for students from non-technical backgrounds to help them catch up.

Fees for Online MCA Programs

Online MCA fees vary depending on the university and program features. Here's a general breakdown:

University

Total Fees (Approx.)

IGNOU

₹48,000 – ₹60,000

Amity University Online

₹1,40,000 – ₹1,60,000

Jain University (Online)

₹1,20,000 – ₹1,50,000

Mangalayatan University Online

₹70,000 – ₹80,000

Manipal University Online

₹1,50,000 – ₹1,80,000

Note: Many universities offer EMI options, scholarships, or early-bird discounts.

Curriculum Overview

The Online MCA curriculum is industry-oriented, covering key topics such as:

Data Structures and Algorithms

Database Management Systems

Cloud Computing

Software Engineering

Machine Learning & AI (in some specializations)

Web and Mobile App Development

Cybersecurity

Capstone Projects & Internships

Some universities also allow students to choose specializations like Data Science, AI & ML, Full Stack Development, or Blockchain.

Career Scope After Online MCA

An Online MCA opens up a wide range of job opportunities in India and abroad. Graduates are equipped with the skills needed in the ever-evolving IT industry.

Popular Career Roles:

Software Developer / Engineer

Data Analyst / Data Scientist

Cloud Solutions Architect

Web & App Developer

Cybersecurity Analyst

IT Consultant

System Analyst

Database Administrator

Top Hiring Companies:

TCS, Infosys, Wipro, Accenture

Google, Amazon, Microsoft

Startups & global remote-first tech firms

Salary Expectations:

Freshers: ₹4 – ₹6 LPA

With 3–5 years experience: ₹8 – ₹15 LPA

Specialized roles (Data Science, AI): ₹12+ LPA

Who Should Consider an Online MCA?

An Online MCA is perfect for:

Working professionals seeking career growth or role-switch

BCA/B.Sc. (CS/IT) graduates looking for flexible higher education

Students from remote areas with limited access to quality institutions

Entrepreneurs planning to launch tech-based startups

Final Thoughts

With top universities offering recognized and affordable Online MCA programs, pursuing this degree online has never been more attractive. Whether you’re aiming for a promotion, a career shift, or just deeper expertise in IT, an Online MCA can be your stepping stone to success.

Ready to upgrade your tech career? Explore UGC-approved Online MCA programs and take the next step in your professional journey — all from the comfort of your home.

Would you like this converted into a downloadable PDF or published format for your blog?

[marcoluther]

Generative AI in Healthcare: Market Trends and Opportunities in 2025

The healthcare industry is experiencing a transformative shift in 2025, driven by the rapid integration of generative artificial intelligence (AI). From accelerating drug discovery to enhancing patient care, generative AI is redefining the boundaries of what's possible in medicine. This blog delves into the current market trends, key applications, and emerging opportunities of generative AI in healthcare.​

Market Overview: A Rapidly Expanding Frontier

The global generative AI in healthcare market is witnessing exponential growth. Valued at approximately USD 1.8 billion in 2023, it's projected to soar to USD 14.8 billion by 2030, reflecting a compound annual growth rate (CAGR) of 36.7% . This surge is fueled by advancements in deep learning, increasing demand for personalized treatments, and the vast availability of healthcare data.​

North America currently leads the market, accounting for over 40% of global revenue in 2023. However, the Asia-Pacific region is emerging as a significant growth area, driven by large patient populations, government initiatives, and rapid technological adoption.

Key Applications of Generative AI in Healthcare

1. Drug Discovery and Development

Generative AI is revolutionizing the pharmaceutical industry by streamlining the drug discovery process. AI algorithms can analyze vast datasets to identify potential drug candidates, predict their efficacy, and optimize molecular structures. This not only accelerates the development timeline but also reduces costs associated with traditional methods.​

Notably, startups like Latent Labs, founded by a former DeepMind scientist, are leveraging generative AI to design synthetic proteins, aiming to make biology "programmable" and enhance computational drug discovery.​

2. Personalized Medicine

Generative AI enables the creation of individualized treatment plans by analyzing genetic, clinical, and lifestyle data. This approach is particularly impactful in fields like oncology and rare disease treatment, where tailored therapeutic strategies are crucial.​

3. Medical Imaging and Diagnostics

AI-powered tools are enhancing the accuracy and speed of medical imaging analysis. For instance, the University of Pittsburgh's CPACE has developed AI tools that produce quick and precise leukemia reports, aiding in faster diagnosis and treatment planning.​

4. Clinical Documentation and Workflow Optimization

Generative AI is streamlining clinical documentation by automating the generation of patient notes, reducing administrative burdens, and allowing healthcare professionals to focus more on patient care.​

5. Mental Health Support

AI-driven mental health tools, such as chatbot-based interventions, have shown effectiveness in improving symptoms of anxiety and depression, especially for mild to moderate cases. These tools offer accessible and scalable mental health support, complementing traditional therapy methods.​

Emerging Opportunities and Innovations

1. Integration with Telemedicine

The fusion of generative AI with telemedicine platforms is enhancing remote diagnostic capabilities. AI-driven tools can assess patient data and medical images, offering real-time insights and recommendations, thereby expanding access to high-quality care in remote and underserved areas.​

2. AI-Powered Virtual Health Assistants

Startups like Doctronic are deploying AI agents as intelligent, fast, and personalized alternatives to traditional online medical searches. These AI agents provide users with possible diagnoses and action plans, including options to book real-time video consultations with licensed medical professionals.​

3. Workforce Training and AI Literacy

Pharmaceutical companies are emphasizing AI literacy among their workforce. For example, Johnson & Johnson has implemented mandatory generative AI training for over 56,000 employees, while Merck has developed a proprietary platform, GPTeal, to facilitate AI usage and productivity.​

Ethical, Legal, and Regulatory Considerations

The integration of generative AI in healthcare raises several ethical and legal questions. Concerns include data privacy, algorithmic biases, and the need for robust regulatory frameworks. International collaborations are essential to develop adaptive policies and regulatory sandboxes to test and refine governance in real-world settings.​

Moreover, ethical principles such as governance, reliability, equity, accountability, traceability, privacy, lawfulness, empathy, and autonomy must be considered to ensure responsible deployment of generative AI in healthcare.​

Conclusion

Generative AI for Healthcare is poised to transform the healthcare landscape by enhancing diagnostic accuracy, personalizing treatment plans, and streamlining clinical workflows. As the technology continues to evolve, it offers unprecedented opportunities to improve patient outcomes and operational efficiency. However, addressing ethical, legal, and regulatory challenges is crucial to ensure the responsible and equitable integration of generative AI in healthcare.

[pranavgill]

Common Applications of Artificial Intelligence in Sales

Artificial intelligence (AI) uses machine learning (ML), natural language processing (NLP), analytics, smart process automation (SPA), conversational artificial intelligence, and deep understanding to learn, solve problems, perform cognitive tasks, and make autonomous decisions akin to a human. The aim of using AI is to delegate some tasks at a higher level than basic automation. Almost all industries nowadays use AI, including sales. Sales include repetitive tasks like generating and following up on leads, lengthy meetings, calling, attending to clients, projecting sales, facilitating operations, and closing deals. AI for sales and marketing integrates tools and capabilities that complement and sometimes replace humans and traditional software in task performance in sales.

Sales forecasting is one of the primary tasks in sales and marketing. The salesperson uses historical data and the prevailing market environment to predict sales. The predictions include prospects likely to convert to sales, target markets or deals to focus on, and prospective clients. Forecasting combines analytical tools and the salesperson's intuition based on skills and experience. AI eases sales forecasting by analyzing the data and generating reports using algorithms and data models with more accuracy than intuition and gut instinct.

Lead generation and conversion are the backbones of successful sales. However, these tasks can be challenging and tedious, especially in competitive niches like care sales and dealerships. One challenge is finding the target demographic and customizing offerings to attract prospects. AI analyzes vast data to identify demographics that suit the company's products and services. Matching these offerings with new leads saves on manual lead generation. The AI sifts through the data using relevant keywords to automatically generate prospects aligned with the business.

AI predicts the likelihood of closing sales from the generated new leads. It further provides information on the prospect's readiness to buy based on behavioral and historical data analysis. This helps salespeople prioritize follow-ups and close more sales.

Companies are rife with meetings among the in-house team and management, customers and prospects, and other groups. Employees spend substantial time summarizing and deducing actions from these meetings. AI tools summarize the content, generate reports, and share them with relevant parties. Summaries may include or recommend actions for the parties.

AI can summarize lengthy sales calls, transcribe conversations, and filter and highlight key topics, objections, and components, such as the probability of closing a deal based on the client's voice tone and responses. Summaries also help salespeople prioritize follow-ups based on previous engagements. AI can decipher speech and text communication on different platforms and perform diverse tasks such as agent deployment, call recording, and note-taking.

Customer service plays a huge role in marketing and often differentiates between competing sales companies dealing in the same product or service. The sales process is communication-intensive, with customers expecting real-time answers to questions. Salespeople may find simultaneously attending to multiple customers challenging and risk mixing up crucial personal and financial information. Conversational AI tools can also help onboarding new customers, suggest new products or features that complement past purchases, and autonomously direct clients to commonly asked questions after a query, with an accompanying prompt to escalate in case more assistance is required. This guarantees round-the-clock customer attendance beyond regular work hours for the sales.

[sranalytics50]

Generative AI for Business: Transforming Industries and Driving Innovation

Introduction

Generative AI is rapidly emerging as a game-changing technology that is revolutionizing how businesses operate across multiple industries. By leveraging advanced artificial intelligence capabilities, companies can unlock unprecedented levels of creativity, efficiency, and personalization.

The Market Potential of Generative AI

The generative AI landscape is experiencing explosive growth. Key market insights include:

Market Size Projection: Expected to reach $207 billion by 2030

Growth Rate: Compound Annual Growth Rate (CAGR) of 24.40% from 2023 to 2030

Industry Perception: 82% of businesses believe generative AI will significantly influence their industry in the next five years

Key Industries Embracing Generative AI

1. Manufacturing

80% of industrial manufacturing leaders report increased productivity

63% note a transformation in work processes

2. Retail

60% of retail companies are investing in AI for personalized customer experiences

Enhancing product recommendations and customer interactions

3. Financial Services

55% exploring AI for fraud detection and risk assessment

Improving financial security and decision-making

4. Healthcare

75% of health system executives view generative AI as revolutionary

Accelerating drug discovery and personalizing patient care

Five Powerful Use Cases for Generative AI

1. Content Creation

Generative AI tools like Jasper, Rytr, and Copy.ai are transforming content production by:

Generating marketing copy

Creating realistic images and illustrations

Streamlining creative processes

2. Personalized Marketing

Platforms such as Preconized and Salesforce Einstein Engage enable:

Tailored marketing campaigns

Individual customer experience optimization

Enhanced brand loyalty through precision targeting

3. Fraud Detection

Solutions like Fraud.net help businesses by:

Identifying transaction anomalies

Assessing financial risks

Protecting organizational assets

4. Drug Discovery

Companies like BenevolentAI and Atomwise are using generative AI to:

Analyze molecular data

Simulate drug compounds

Accelerate medical research

5. Product Design

Tools such as Autodesk Dreamcatcher facilitate:

Innovative product development

Performance optimization

Personalized product recommendations

Main Benefits for Businesses

Creative Innovation

Generative AI breaks traditional creative barriers by:

Generating fresh ideas

Designing innovative concepts

Capturing emerging market trends

Enhanced Customer Experiences

AI enables hyper-personalization through:

Tailored product recommendations

Customized marketing interactions

Data-driven customer insights

Informed Decision Making

By analyzing vast datasets, generative AI supports:

Risk assessment

Market trend prediction

Operational process optimization

Implementation Considerations

Key Steps

Define clear business goals

Assess data infrastructure

Select appropriate AI algorithms

Establish a robust development environment

Train and validate models

Integrate with existing systems

Continuous monitoring and improvement

Potential Challenges

Data privacy and quality

Computational complexity

Model interpretability

Seamless system integration

Conclusion

Generative AI represents more than just a technological trend—it's a transformative force reshaping business landscapes. By adopting a strategic, human-centric approach, organizations can harness this powerful technology to drive innovation, efficiency, and competitive advantage.

As the field continues to evolve, businesses that proactively explore and implement generative AI will be best positioned to thrive in an increasingly digital and intelligent marketplace.

[felix-jake]

The Future of Land Surveying: How Technology is Revolutionizing the Industry

Land surveying has undergone much revolution in technology concerning construction, infrastructural projects, and property developments. Gone are the days of physical measurement in traditional methodologies by hand; currently, innovation offers precision with time and money effectiveness. In fact, the core issue covered is how modern-day technological impacts might influence land surveying prospects into the future as well as changes to be noted within this sphere.

1. Land Surveying Revolution

Land surveying dates back to ancient times, where the Egyptians and Romans used basic tools to measure land. Through time, the industry has developed with the invention of theodolites, total stations, and GPS technology. However, the digital revolution and the advent of AI, automation, and remote sensing have dramatically changed how land surveys are conducted today.

2. Cutting-Edge Technologies Reshaping the Industry

A. Drones and Aerial Surveying

Drones, or UAVs, are one of the most important technological breakthroughs in land surveying. Drones can be equipped with high-resolution cameras, LiDAR sensors, and GPS technologies to capture pictures of vast terrains that take a much lesser time span as compared to ground surveys.

Benefits of using drones for land surveying: 

Speed data collection; efficient

Accuracy improved with high resolution

Economic as compared to traditional ground survey.

These are to penetrate hazardous or inaccessible places.

B. LiDAR Technology 

The other land survey game-changer is Light Detection and Ranging, abbreviated as LiDAR technology. The distance measurement through the LiDAR sensor can be obtained by the emission of laser pulse. It is precise to acquire the 3-D models of topography, man-made structures, and vegetation.

High-precision Topographic Mapping; Forest and vegetation analysis; infrastructure and urban planning; Flood Risk Assessment and disaster management.

LiDAR, on its own, helps in mapping large areas with efficiency and generating real-time data for better decision-making.

C. Global Navigation Satellite Systems (GNSS)

The GNSS, GPS, GLONASS, and Galileo, have been established as indispensable tools in land surveying. The systems provide geospatial information with centimeter-level precision that eliminates human errors and manual calculations.

Benefits of GNSS technology:

- Real-time positioning and georeferencing

- Improved survey accuracy

- Reduced reliance on the traditional benchmark

- GIS and CAD compatible

D. Geographic Information Systems (GIS) and Big Data

The introduction of GIS technology has changed the way survey data is processed and presented. The GIS allows for the storage, handling, and processing of spatial data, thus becoming a tool in urban planning, environmental monitoring, and land development.

Benefits of the application of GIS in land surveying

Sophisticated mapping and visualization

Combining different sources for enhanced analysis

Better decision making in construction and real estate

Land use changes and environmental change in real-time

 E. Artificial Intelligence (AI) and Machine Learning

Artificial intelligence and machine learning algorithms contribute significantly to automation in the processes of data analysis, pattern recognition, and predictive modeling in land surveying.

Applications of AI in surveying:

- Automatic extraction of features from aerial images

- Predictive models for land development

- Enhanced precision in boundary detection

- Minimization of human errors in survey calculations

With the advancement of AI technology, its application in land surveying is expected to increase, making processes more efficient and data-driven.

3. The Impact of Technology on Land Surveying

The integration of these technologies has transformed land surveying in multiple ways:

1. Improved Accuracy:

Thanks to the LiDAR, GNSS, and AI-driven software tools, much lesser errors and discrepancies have resulted in accurate measurements of surveyors.

2. Time Efficiency: 

Use of drones and automated processing reduces the time spent to complete the survey, therefore quicker execution of the project

3. Cost Efficiency:

Automated tools and AI-driven analysis also ensure that money spent on labor, equipment, and time is minimized by the company.

4. Accessibility:

The invention of drones and satellite technology makes it possible to reach inaccessible, dangerous, or otherwise unreachable areas.

5. Data Integration and Visualization:

GIS and big data analytics make visualization of land data possible, which can then be used for making decisions in urban planning and infrastructure projects.

4. Challenges and Future Outlook

Adoption of new technologies in land surveying comes with its challenges, however positive the advantages may be:

- Significant Capital Expenditure: A huge one for building drones, LiDAR equipment, and AI-driven software.

- Technical Expertise:The surveyor must have professional training to handle such complex equipment and data themselves

- Mandatory Regulations and Privacy Issues:Drone and remote sensing technologies require strong regulation and privacy concerns.

- Cyber Security in Managing Huge Volumes of Geospatial Data: It requires a strong approach to cybersecurity.

Land surveying of the future is likely to move upwards on automation, cloud storage of data, and AI power. Trends emerging include blockchain in safe land record management and augmented reality in interactive land surveying.

5. Conclusion

Technology is revolutionizing land surveying at a pace like no other time, making it more productive, accurate, and cost-effective. From drones and LiDAR to AI and GIS, all these are leading the industry to a more data-driven and automated future. With the challenges, innovation and adaptation will continue to ensure that land surveying is a critical part of modern infrastructure and development. It is important that professionals in this industry evolve with such change as technological improvement will continue moving forward in order to remain competitive and offer the best possible surveying solutions for an ever-changing world.

[blogscollection]

Enhancing Creativity and Collaboration: A Guide to Brainstorming with Visily AI

In today’s dynamic work environment, creativity and collaboration are essential for success. Whether designing an innovative product, solving a complex problem, or developing a game-changing strategy, brainstorming remains a powerful tool. With advancements in technology, tools like Visily AI are transforming how teams brainstorm, making the process more efficient, engaging, and productive.

What is Visily AI?

Visily AI is a cutting-edge brainstorming and wireframing tool designed to empower teams and individuals to bring their ideas to life. With features tailored for creativity and collaboration, it simplifies complex workflows and enhances team synergy. By combining AI-driven insights with user-friendly design tools, Visily AI caters to various industries, including software development, marketing, education, and more.

The Challenges of Traditional Brainstorming

Traditional brainstorming, though effective, often comes with challenges. Teams may struggle with unorganized discussions, difficulty visualizing ideas, and an inability to document insights effectively. Additionally, remote work has introduced new hurdles, as participants often feel disconnected or unable to contribute in real-time. This is where Visily AI makes a difference, bridging the gap between creativity and structure.

Features That Redefine Brainstorming

1. AI-Powered Idea Generation

Visily AI’s machine learning algorithms suggest innovative ideas based on the user’s input. Whether you’re designing a user interface or creating a marketing campaign, the AI generates relevant concepts and inspirations, accelerating the creative process.

2. Collaborative Workspaces

The platform enables real-time collaboration, ensuring team members can contribute simultaneously, regardless of their location. With intuitive tools like sticky notes, sketching options, and voting mechanisms, brainstorming sessions become more interactive and inclusive.

3. Wireframing Made Easy

One of Visily AI’s standout features is its ability to transform ideas into tangible designs. Teams can quickly create wireframes or prototypes, helping visualize concepts and refine them collaboratively. This feature is particularly beneficial for product designers and developers.

4. Intelligent Documentation

Brainstorming sessions often result in a flood of ideas that can be hard to organize. Visily AI automatically categorizes and prioritizes insights, ensuring nothing gets lost in the shuffle. This streamlined approach saves time and enhances productivity.

5. Seamless Integration

Visily AI integrates effortlessly with popular project management and design tools, allowing teams to transition smoothly from brainstorming to execution. Whether you're using Slack, Jira, or Figma, Visily AI ensures continuity in your workflows.

How Visily AI Boosts Creativity

By leveraging AI technology, Visily AI minimizes cognitive load, enabling participants to focus on creative thinking rather than administrative tasks. Teams can explore unconventional ideas, iterate quickly, and gain valuable insights that drive innovation. Moreover, its intuitive interface ensures that users of all skill levels can contribute effectively.

The Future of Brainstorming

As technology continues to evolve, tools like Visily AI represent the future of brainstorming. By combining artificial intelligence with user-centric design, it fosters a culture of innovation and collaboration. Whether you’re a startup founder, a creative professional, or part of a large organization, adopting Visily AI can revolutionize how your team approaches challenges and generates ideas.

Conclusion

Visily AI is not just a tool; it’s a game-changer for teams looking to maximize their creative potential. With its AI-powered features, collaborative capabilities, and intuitive design, it transforms brainstorming from a chaotic process into a streamlined experience. Embracing tools like Visily AI can lead to better ideas, stronger teamwork, and faster innovation. So why wait? Dive into the future of brainstorming and let Visily AI unlock your team’s true creative potential.

[amairadutta]

Artificial Intelligence in E-Commerce

With the increasing internet penetration, the e-commerce Business is flourishing in India. E-commerce firms in India have been in a cut-throat rivalry, all of them striving hard to outdo one another. Several successful e-commerce organizations are deploying Artificial Intelligenceto build improved products, user expertise, smarter logistics, target the ideal demographics, and also be the favored alternative for their clients. They realize that distinction is essential to their survival in a challenging sector.

Let’s analyze the Best 3 E-Commerce companies in India by market share– Flipkart, Amazon India, and Myntra are utilizing AI. According to the estimates of India’s Economic Times, Flipkart and its own independently-run subsidiary Myntra, collectively own a market share of 38.5 percent while Flipkart alone has a talk of about 34%. Amazon India constitutes a 29% market share. It may be noted that these are privately-held businesses that do not share their earnings or market share numbers and frequently dispute the amounts the analysts and media companies generate.

FLIPKART – Partnership with Microsoft

Microsoft to supply customers in India much better internet shopping services. As a primary step from the wide collaboration between both businesses, Flipkart has embraced Microsoft Azure, the private cloud system.

Flipkart stated in a statement it intends to leverage AI, Machine Learning and analytics capabilities in Azure, including Cortana Intelligence Suite and Power BI, to maximize its information for advanced merchandising, advertising, and client support.

Flipkart’s project MIRA is directed to give an offline experience to online shopping. The job seems to be an answer to Flipkart’s reported 10-11% yield rate. The aim of this project would be to scale the in-store experience of owning a sales partner — but through artificial intelligence and via electronic channels. Flipkart’s customers with broad purposes (looking for, say, shoes or bedsheets) are directed with pertinent queries, conversational filters, shopping ideas, and trending groups.

Flipkart can expand its own existence at Silicon Valley in the United States and focus on AI-based goods by taking advantage of world-class research centers in the Valley.

Amazon India – India business operations

Amazon is dedicated to Long-Term Investment in Technology and infrastructure in India, where AI is a crucial technology it’s spent in. Amazon India has employed machine learning and AI at a number of places. Described are a number of these areas:

Correcting Addresses

Addresses in India aren’t well Structured and frequently users enter incorrect addresses (e.g. incorrect pin code or town name) or addresses with lost data (e.g. missing road name). Incorrect addresses cause bundles to miss delivery dates and result in unsuccessful deliveries. The business has been utilizing machine learning methods to detect spam addresses, calculate address quality scores, and right city-pin code mismatches, and supply tips for users to fix wrong addresses.

Catalog defects

Item catalog defects like missing attributes such as brand, color or poor-quality images can negatively affect Consumer Experience. The business is utilizing AI and machine learning to extract missing feature information including brand or color from merchandise titles and graphics.

Product Size Recommendations

In groups such as sneakers and other apparel, distinct brands frequently have different size traditions. By way of instance, a catalog size 6 might correspond to a physical dimension of 15 cm to get Reebok while for Nike a catalog size 6 might correspond to a physical dimension of 16 cm.

Deals for Occasions

Machine learning is accustomed to identifying typically the products which are in high demand or get high quantities of lookup queries and review sites throughout the festival period. Machine Learning algorithms also forecast the discounts and deals to provide the goods to attain a certain sales prediction that assists in better preparation.

System on previous holiday purchase information and present purchase action, a system could have the ability to calibrate the demand more correctly so as to sell goods at the ideal costs to either (a) move specific items at large quantity, or (b) maximize profit margins by proposing the maximum margin products to consumers throughout the festival season.

Myntra (acquired by Flipkart)

The AI initiatives of fashion e-tailer Myntra are centered around three verticals, i.e., Product, Expertise, and Logistics

Product/ Merchandise

Myntra enables intelligent fashion via its AI platform called ‘Rapid’. ‘Fast Fashion’ or ‘Quick fashion’ is described as a modern term used by fashion retailers to state that design proceeds from catwalk fast enough to catch the present trends in fashion.

This can dramatically decrease the time required to make a style merchandise to a few weeks in the generally long 9-14 weeks’ lifecycle. According to the available sales statistics, the technology figures out exactly what characteristics are selling. Then, according to this, the designers begin producing the trend products. Myntra appears to have gone a step farther in mechanically designing its own style solutions. This February, they found completely machine generated layouts for T-shirts.

Expertise

Myntra is utilizing machine Learning to enhance the payment. As per an analysis from the Institute for Business in the International Context (IBGC), online payments trades typically neglect in India because of 2 different set of explanations.

2.The Second is the banks, that supply obtaining services in almost any payment transactions, are inclined to have poor IT systems. The financial programs in India could be down to anywhere around 4-5 hours at a time, making the experience incredibly frustrating for clients. Machine learning facilitates information regarding the best Payment gateway the payment has to be hauled through, hence making the process of paying more convenient, faster, and less frustrating.

Myntra additionally enriches the user experience by providing the proper recommendations based on which a client has seen or purchased previously. It utilizes “collaborative filtering, which prompts product recommendations to a single individual based on what another individual has recently bought and helps match the styles which go well with specific products.

Logistics

As clients often complain about overdue refunds, Myntra would like to create its returns policy much more effective as it considers that yields are an essential component of the fashion sector — that depends upon dimensions, matches, and tastes which make yields more prevalent than other businesses (distinguished products like apparel have a tendency to have higher yield rates than undifferentiated goods).

By assessing a client’s previous returns routines, Myntra’s ‘Sabre’, which is an AI-based system that would enable quicker refunds to buyers that have a good buying and return record in the past.

It needs to be noted that businesses on the scale of Myntra (i.e. big online retailers) have been in the ideal place to detect fraud, even since they have the most cases of both valid and fraudulent activity on documents, letting them train machine learning methods to forecast fraud based on stronger contextual and historical information.

Myntra is also planning to decrease its rate of return. A greater RTO translates into greater reductions because lots of cash on-delivery (COD) orders aren’t sent for a variety of reasons such as clients not being present or not getting money at the point in time. Some clients are said to provide incredible or irrational reasons for putting their yields.

This type of customer support “intervention” strategy is the business can enable intervening to stop refunds or client churn. It is suspected that this might become common among many businesses in recent years ahead, especially in B2C industries with huge volumes.

Author : Exito

[commercepulseenterprise]

AI Meeting Notes: Revolutionizing Workplace Documentation

The days of scribbling notes on pen and paper during meetings and now forgetting what was discussed are gone. Workplace productivity is now at a high owing to the development of artificial intelligence (AI). AI-powered meeting notes are one such innovation that is changing the document, shared, and accessed information. These tools allow for automatic note-taking, which is less error-prone, significantly faster, and as accessible as desired while freeing up your team's brain power for more strategic work. So, how does it work, and if you should adopt it? Let’s dive in.

What Are AI Meeting Notes?

AI meeting notes are also automated, or technology-driven, summaries of the conversations held in a meeting. Using AI algorithms, these tools are capable of real-time transcription of conversations, summarizing the main points, and organizing the data into useful insights. While traditional note-taking technique leaves scope for missing vital facts, AI tools handle better by eliminating this liability and coming up with an accurate and objective record.

Key Features:

Real-time transcription: AI is a listener that instantaneously transcribes speech.

Actionable insights: It extracts action items, deadlines, and follow-ups.

Integration capabilities: It works as a seamless sync with calendars project management systems, and collaboration tools.

AI Meeting Notes: The Technology Behind It.

AI meeting note tools rely on advanced technologies such as:

Natural Language Processing (NLP): It helps AI interpret and understand human speech, making the transcription more contextually accurate.

Machine Learning: It learns to continuously improve the quality of transcriptions from past inputs.

Voice-to-Text Conversion: It converts spoken words into text in real time, allowing for seamless documentation.

The combination of these technologies yields meeting summaries which are immensely accurate and context aware which are in a league of their own compared to manual effort.

Benefits of Using AI for Meeting Notes

Why should businesses invest in AI meeting notes? Let’s explore some key benefits:

Improved Accuracy and Reliability

It also eliminates the risk of human error. It becomes forgotten action items, misheard phrases, or incomplete summaries.

Beyond this, many AI tools are designed to understand accents and industry-specific jargon, making it more reliable.

Enhanced Collaboration

With AI-generated notes stored digitally, teams can access, edit, and share meeting content effortlessly.

Searchable summaries make it easier to revisit specific points from previous meetings.

Real-Time Capabilities

Some tools transcribe and summarize discussions as they happen, enabling participants to stay focused on the conversation rather than taking notes.

Key Features of AI-Powered Meeting Note Tools

The best AI meeting note tools come with a range of features tailored to different needs:

Voice Recognition: Speaks accurately, and identifies and transcribes the speaker's words..

Summarization: It offers a concise and plain summation of protracted arguments.

Customizable Templates: It lets users format notes for any given purpose: legal documentation, updates, etc.

Real-Life Applications of AI Meeting Notes

AI meeting notes are proving indispensable across various industries and use cases:

Remote Work Settings

Teams working across different time zones can easily access and review notes, eliminating the need for synchronous updates.

Enhancing Team Collaboration

AI tools provide centralized documentation, ensuring everyone is aligned on decisions, action items, and deadlines.

Specialized Industries

Legal Industry: Lawyers use AI notes for precise documentation of client meetings or court hearings.

Healthcare: Doctors rely on AI transcription to document patient consultations efficiently.

Conclusion

The AI meeting notes are a groundbreaking shift, that businesses have been running from for decades. These tools automate time-consuming tasks, ensuring accuracy, and allowing teams to work together more collaboratively, so they can get back to the real work by focusing on what matters, innovation and goal achievement. There’s no reason to be trying to catch up with technology when it comes to AI meeting notes — it’s about creating a foundation for future-ready productivity. Time to revitalize your meetings? It’s time to embrace AI!

[pattem]

A Deep Dive into the Java Weka ML Library

Listing some Pivotal Points of Java Libraries and their Merits 

Java is an object-oriented programming language that can be used as a platform in itself. It is a fast, secure, and reliable programming language used for developing more innovative and interactive applications, from mobile applications and enterprise software to server-side technologies and big-data applications. To make the development more effective and easier, specific tools, libraries, and frameworks are used. By choosing the perfect library for the project, companies can streamline the development process, enhance code efficiency and quality, and also speed up the company’s development lifecycle. These libraries are very essential in the developer’s toolkit, which significantly improves code maintainability and productivity. 

A Java library is a collection of classes that were previously pre-written by somebody. Jackson, Apache, Maven, JUnit, and Google JSON are some of the most widely used Java libraries. Moreover, Java provides a lot of libraries, except for standard libraries. Java ML libraries can also be used to make the process much more effective and easier. Java ML is a collection of data mining and machine learning algorithms that include a common interface for each. In this blog, we will learn some magnificent insights about Java ML libraries and their key features and advantages. So, without wasting any further time, let us jump into the blog!

Peregrinating Java ML Libraries and Their Key Benefits and Advantages 

Generally, WEKA offers a wide range of machine learning and data mining algorithms. These, combined with the user-friendly and adaptable interface, make it an ideal choice for generative AI and academic research rather than traditional applications such as regression, classification, and clustering. Moreover, WEKA, specifically abbreviated as Waikato Environment for Knowledge Analysis, is a very popular and widely used software suite for machine learning and data mining tasks. It also provides a user-friendly graphical interface for numerous algorithms. Furthermore, the graphical user interface of WEKA makes it easy to prototype and test characteristic algorithms. The user-friendly environments enable companies to experiment with visualizing results in different settings and understand the behavior of the algorithm. WEKA offers multiple data filters like normalization, cleansing, and transformation, along with specific tools to explore and build new features before applying machine learning algorithms. 

WEKA provides a booming Java Application Program Interface (API), which is very beneficial in integrating custom algorithms into larger Java-based workflows and applications. Moreover, it also provides tools to evaluate and integrate the algorithm's performance. This capacity is crucial to comparing approaches against some established methods using the regulated evaluation metrics. In addition to that, WEKA possesses a diverse collection of algorithms that cover supervised learning, unsupervised learning, and evaluation metrics. This versatility allows companies to tackle numerous machine learning problems without the help of any particular tool. Being an open-source technology offers customization, transparency, and community support. WEKA runs effortlessly on Windows, MacOS, and Linux, offering great flexibility for multiple computing environments. 

Noting Down some Key features and Key Takeaways from the Blog

WEKA is the perfect technology to be used for academic research and personal projects, as it completely eliminates license costs. Moreover, it can also be used for diverse ML solutions, including regression, classification, dimensionality reduction, clustering, and more. Preferring the best Java development services company also plays an important role in a company’s elevation and growth. 

Pattem Digital is the leading Java development services company that provides its clients with the best feature-rich applications in the industry. We possess a combined team of talented professionals to make their applications more innovative and user-friendly. We also make sure to provide our clients with applications that meet their business needs and user requirements. We also maintain an impeccable track record of customer satisfaction.

[sunaleisocial]

Creating and verifying stable AI-controlled systems in a rigorous and flexible way

New Post has been published on https://sunalei.org/news/creating-and-verifying-stable-ai-controlled-systems-in-a-rigorous-and-flexible-way/

Creating and verifying stable AI-controlled systems in a rigorous and flexible way

Neural networks have made a seismic impact on how engineers design controllers for robots, catalyzing more adaptive and efficient machines. Still, these brain-like machine-learning systems are a double-edged sword: Their complexity makes them powerful, but it also makes it difficult to guarantee that a robot powered by a neural network will safely accomplish its task.

The traditional way to verify safety and stability is through techniques called Lyapunov functions. If you can find a Lyapunov function whose value consistently decreases, then you can know that unsafe or unstable situations associated with higher values will never happen. For robots controlled by neural networks, though, prior approaches for verifying Lyapunov conditions didn’t scale well to complex machines.

Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and elsewhere have now developed new techniques that rigorously certify Lyapunov calculations in more elaborate systems. Their algorithm efficiently searches for and verifies a Lyapunov function, providing a stability guarantee for the system. This approach could potentially enable safer deployment of robots and autonomous vehicles, including aircraft and spacecraft.

To outperform previous algorithms, the researchers found a frugal shortcut to the training and verification process. They generated cheaper counterexamples — for example, adversarial data from sensors that could’ve thrown off the controller — and then optimized the robotic system to account for them. Understanding these edge cases helped machines learn how to handle challenging circumstances, which enabled them to operate safely in a wider range of conditions than previously possible. Then, they developed a novel verification formulation that enables the use of a scalable neural network verifier, α,β-CROWN, to provide rigorous worst-case scenario guarantees beyond the counterexamples.

“We’ve seen some impressive empirical performances in AI-controlled machines like humanoids and robotic dogs, but these AI controllers lack the formal guarantees that are crucial for safety-critical systems,” says Lujie Yang, MIT electrical engineering and computer science (EECS) PhD student and CSAIL affiliate who is a co-lead author of a new paper on the project alongside Toyota Research Institute researcher Hongkai Dai SM ’12, PhD ’16. “Our work bridges the gap between that level of performance from neural network controllers and the safety guarantees needed to deploy more complex neural network controllers in the real world,” notes Yang.

For a digital demonstration, the team simulated how a quadrotor drone with lidar sensors would stabilize in a two-dimensional environment. Their algorithm successfully guided the drone to a stable hover position, using only the limited environmental information provided by the lidar sensors. In two other experiments, their approach enabled the stable operation of two simulated robotic systems over a wider range of conditions: an inverted pendulum and a path-tracking vehicle. These experiments, though modest, are relatively more complex than what the neural network verification community could have done before, especially because they included sensor models.

“Unlike common machine learning problems, the rigorous use of neural networks as Lyapunov functions requires solving hard global optimization problems, and thus scalability is the key bottleneck,” says Sicun Gao, associate professor of computer science and engineering at the University of California at San Diego, who wasn’t involved in this work. “The current work makes an important contribution by developing algorithmic approaches that are much better tailored to the particular use of neural networks as Lyapunov functions in control problems. It achieves impressive improvement in scalability and the quality of solutions over existing approaches. The work opens up exciting directions for further development of optimization algorithms for neural Lyapunov methods and the rigorous use of deep learning in control and robotics in general.”

Yang and her colleagues’ stability approach has potential wide-ranging applications where guaranteeing safety is crucial. It could help ensure a smoother ride for autonomous vehicles, like aircraft and spacecraft. Likewise, a drone delivering items or mapping out different terrains could benefit from such safety guarantees.

The techniques developed here are very general and aren’t just specific to robotics; the same techniques could potentially assist with other applications, such as biomedicine and industrial processing, in the future.

While the technique is an upgrade from prior works in terms of scalability, the researchers are exploring how it can perform better in systems with higher dimensions. They’d also like to account for data beyond lidar readings, like images and point clouds.

As a future research direction, the team would like to provide the same stability guarantees for systems that are in uncertain environments and subject to disturbances. For instance, if a drone faces a strong gust of wind, Yang and her colleagues want to ensure it’ll still fly steadily and complete the desired task. 

Also, they intend to apply their method to optimization problems, where the goal would be to minimize the time and distance a robot needs to complete a task while remaining steady. They plan to extend their technique to humanoids and other real-world machines, where a robot needs to stay stable while making contact with its surroundings.

Russ Tedrake, the Toyota Professor of EECS, Aeronautics and Astronautics, and Mechanical Engineering at MIT, vice president of robotics research at TRI, and CSAIL member, is a senior author of this research. The paper also credits University of California at Los Angeles PhD student Zhouxing Shi and associate professor Cho-Jui Hsieh, as well as University of Illinois Urbana-Champaign assistant professor Huan Zhang. Their work was supported, in part, by Amazon, the National Science Foundation, the Office of Naval Research, and the AI2050 program at Schmidt Sciences. The researchers’ paper will be presented at the 2024 International Conference on Machine Learning.