#Artificial Intelligence & Data Science course

[cbirt]

🚀 Machine Learning is revolutionizing how we analyze and interpret biological data. 💡 ⚡ As Machine Learning continues to evolve, its integration with biological sciences promises to accelerate discoveries and lead to breakthroughs in health, medicine, and biotechnology. 🎯 Biological data comes in many forms: DNA sequences, protein interactions, metabolic pathways, and more. Machine Learning models can handle these vast datasets, identifying relationships and patterns that help scientists understand the underlying biology. ⭐ Machine Learning isn’t just a tool—it’s the future of biological discovery. The possibilities are limitless! 🚀 Want to learn how to analyze biological big data using machine learning? ⚡📢 Join our training program on "Machine Learning in Bioinformatics" 🗓 Dates August 21-31, 2024 ⏰ Time: 6:30 PM - 7:30 PM IST 🗓 Deadline to Apply Extended to: August 20, 2024 ⌛ Duration: 10 Days 💻 Mode: Online Ready to revolutionize your skills? 👉 𝐒𝐞𝐜𝐮𝐫𝐞 𝐘𝐨𝐮𝐫 𝐒𝐩𝐨𝐭 𝐓𝐨𝐝𝐚𝐲: https://lnkd.in/grUEakiP For more details/queries, contact: info@cbirt.net

[jcmarchi]

Elaine Liu: Charging ahead

New Post has been published on https://thedigitalinsider.com/elaine-liu-charging-ahead/

Elaine Liu: Charging ahead

MIT senior Elaine Siyu Liu doesn’t own an electric car, or any car. But she sees the impact of electric vehicles (EVs) and renewables on the grid as two pieces of an energy puzzle she wants to solve.

The U.S. Department of Energy reports that the number of public and private EV charging ports nearly doubled in the past three years, and many more are in the works. Users expect to plug in at their convenience, charge up, and drive away. But what if the grid can’t handle it?

Electricity demand, long stagnant in the United States, has spiked due to EVs, data centers that drive artificial intelligence, and industry. Grid planners forecast an increase of 2.6 percent to 4.7 percent in electricity demand over the next five years, according to data reported to federal regulators. Everyone from EV charging-station operators to utility-system operators needs help navigating a system in flux.

That’s where Liu’s work comes in.

Liu, who is studying mathematics and electrical engineering and computer science (EECS), is interested in distribution — how to get electricity from a centralized location to consumers. “I see power systems as a good venue for theoretical research as an application tool,” she says. “I’m interested in it because I’m familiar with the optimization and probability techniques used to map this level of problem.”

Liu grew up in Beijing, then after middle school moved with her parents to Canada and enrolled in a prep school in Oakville, Ontario, 30 miles outside Toronto.

Liu stumbled upon an opportunity to take part in a regional math competition and eventually started a math club, but at the time, the school’s culture surrounding math surprised her. Being exposed to what seemed to be some students’ aversion to math, she says, “I don’t think my feelings about math changed. I think my feelings about how people feel about math changed.”

Liu brought her passion for math to MIT. The summer after her sophomore year, she took on the first of the two Undergraduate Research Opportunity Program projects she completed with electric power system expert Marija Ilić, a joint adjunct professor in EECS and a senior research scientist at the MIT Laboratory for Information and Decision Systems.

Predicting the grid

Since 2022, with the help of funding from the MIT Energy Initiative (MITEI), Liu has been working with Ilić on identifying ways in which the grid is challenged.

One factor is the addition of renewables to the energy pipeline. A gap in wind or sun might cause a lag in power generation. If this lag occurs during peak demand, it could mean trouble for a grid already taxed by extreme weather and other unforeseen events.

If you think of the grid as a network of dozens of interconnected parts, once an element in the network fails — say, a tree downs a transmission line — the electricity that used to go through that line needs to be rerouted. This may overload other lines, creating what’s known as a cascade failure.

“This all happens really quickly and has very large downstream effects,” Liu says. “Millions of people will have instant blackouts.”

Even if the system can handle a single downed line, Liu notes that “the nuance is that there are now a lot of renewables, and renewables are less predictable. You can’t predict a gap in wind or sun. When such things happen, there’s suddenly not enough generation and too much demand. So the same kind of failure would happen, but on a larger and more uncontrollable scale.”

Renewables’ varying output has the added complication of causing voltage fluctuations. “We plug in our devices expecting a voltage of 110, but because of oscillations, you will never get exactly 110,” Liu says. “So even when you can deliver enough electricity, if you can’t deliver it at the specific voltage level that is required, that’s a problem.”

Liu and Ilić are building a model to predict how and when the grid might fail. Lacking access to privatized data, Liu runs her models with European industry data and test cases made available to universities. “I have a fake power grid that I run my experiments on,” she says. “You can take the same tool and run it on the real power grid.”

Liu’s model predicts cascade failures as they evolve. Supply from a wind generator, for example, might drop precipitously over the course of an hour. The model analyzes which substations and which households will be affected. “After we know we need to do something, this prediction tool can enable system operators to strategically intervene ahead of time,” Liu says.

Dictating price and power

Last year, Liu turned her attention to EVs, which provide a different kind of challenge than renewables.

In 2022, S&P Global reported that lawmakers argued that the U.S. Federal Energy Regulatory Commission’s (FERC) wholesale power rate structure was unfair for EV charging station operators.

In addition to operators paying by the kilowatt-hour, some also pay more for electricity during peak demand hours. Only a few EVs charging up during those hours could result in higher costs for the operator even if their overall energy use is low.

Anticipating how much power EVs will need is more complex than predicting energy needed for, say, heating and cooling. Unlike buildings, EVs move around, making it difficult to predict energy consumption at any given time. “If users don’t like the price at one charging station or how long the line is, they’ll go somewhere else,” Liu says. “Where to allocate EV chargers is a problem that a lot of people are dealing with right now.”

One approach would be for FERC to dictate to EV users when and where to charge and what price they’ll pay. To Liu, this isn’t an attractive option. “No one likes to be told what to do,” she says.

Liu is looking at optimizing a market-based solution that would be acceptable to top-level energy producers — wind and solar farms and nuclear plants — all the way down to the municipal aggregators that secure electricity at competitive rates and oversee distribution to the consumer.

Analyzing the location, movement, and behavior patterns of all the EVs driven daily in Boston and other major energy hubs, she notes, could help demand aggregators determine where to place EV chargers and how much to charge consumers, akin to Walmart deciding how much to mark up wholesale eggs in different markets.

Last year, Liu presented the work at MITEI’s annual research conference. This spring, Liu and Ilić are submitting a paper on the market optimization analysis to a journal of the Institute of Electrical and Electronics Engineers.

Liu has come to terms with her early introduction to attitudes toward STEM that struck her as markedly different from those in China. She says, “I think the (prep) school had a very strong ‘math is for nerds’ vibe, especially for girls. There was a ‘why are you giving yourself more work?’ kind of mentality. But over time, I just learned to disregard that.”

After graduation, Liu, the only undergraduate researcher in Ilić’s MIT Electric Energy Systems Group, plans to apply to fellowships and graduate programs in EECS, applied math, and operations research.

Based on her analysis, Liu says that the market could effectively determine the price and availability of charging stations. Offering incentives for EV owners to charge during the day instead of at night when demand is high could help avoid grid overload and prevent extra costs to operators. “People would still retain the ability to go to a different charging station if they chose to,” she says. “I’m arguing that this works.”

[aarvyedutech]

TOP 10 courses that have generally been in high demand in 2024-

Data Science and Machine Learning: Skills in data analysis, machine learning, and artificial intelligence are highly sought after in various industries.

Cybersecurity: With the increasing frequency of cyber threats, cybersecurity skills are crucial to protect sensitive information.

Cloud Computing: As businesses transition to cloud-based solutions, professionals with expertise in cloud computing, like AWS or Azure, are in high demand.

Digital Marketing: In the age of online businesses, digital marketing skills, including SEO, social media marketing, and content marketing, are highly valued.

Programming and Software Development: Proficiency in programming languages and software development skills continue to be in high demand across industries.

Healthcare and Nursing: Courses related to healthcare and nursing, especially those addressing specific needs like telemedicine, have seen increased demand.

Project Management: Project management skills are crucial in various sectors, and certifications like PMP (Project Management Professional) are highly valued.

Artificial Intelligence (AI) and Robotics: AI and robotics courses are sought after as businesses explore automation and intelligent technologies.

Blockchain Technology: With applications beyond cryptocurrencies, blockchain technology courses are gaining popularity in various sectors, including finance and supply chain.

Environmental Science and Sustainability: Courses focusing on environmental sustainability and green technologies are increasingly relevant in addressing global challenges.

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[drax0001]

The Power of Python

In the ever-evolving world of technology, programming skills are in high demand, and Python has emerged as one of the most popular languages to learn. Whether you’re a beginner exploring the tech field or a seasoned professional looking to upskill, learning Python offers numerous benefits. Here's why Python is a must-learn language for anyone stepping into the tech ecosystem.

1. Beginner-Friendly Syntax

Python is renowned for its simplicity and readability, making it an ideal choice for beginners. The syntax is straightforward and closely resembles the English language, reducing the learning curve for new programmers. This user-friendly nature allows learners to focus on problem-solving rather than struggling with complex syntax.

2. Versatility Across Industries

Python is a general-purpose programming language used across various industries. From web development to data science, artificial intelligence (AI), machine learning (ML), automation, and even game development, Python’s versatility makes it an invaluable skill in today’s job market.

3. Extensive Libraries and Frameworks

Python’s rich ecosystem of libraries and frameworks simplifies complex tasks. For example:

Pandas and NumPy for data analysis

TensorFlow and PyTorch for machine learning

Flask and Django for web development

Matplotlib and Seaborn for data visualization These tools empower developers to build robust applications efficiently.

4. High Demand in the Job Market

Python is one of the most sought-after skills in the tech industry. Roles like data scientists, machine learning engineers, software developers, and web developers often list Python as a required skill. Learning Python can significantly enhance your employability and open doors to lucrative career opportunities.

5. Supportive Community and Resources

Python boasts a vast and active community of developers worldwide. This community ensures that learners and professionals have access to ample resources, tutorials, forums, and documentation. No matter the challenge, you’re likely to find solutions or guidance from fellow Python enthusiasts.

6. Automation Made Easy

Python simplifies the process of automating repetitive tasks, saving time and increasing productivity. With libraries like Selenium and BeautifulSoup, you can automate tasks such as web scraping, data entry, and testing, enhancing efficiency in personal and professional projects.

7. Platform Independence

Python is a cross-platform language, meaning you can write and run Python code on various operating systems, including Windows, macOS, and Linux. This flexibility ensures that your skills are applicable across diverse environments.

8. Great for Data Science and AI

In the age of big data, Python is a cornerstone language for data science and AI. Its ease of use, coupled with powerful libraries like Scikit-learn, Keras, and OpenCV, makes it the go-to choice for data-driven fields. Python’s capabilities allow professionals to analyze data, build predictive models, and create intelligent systems effectively.

9. Encourages Problem-Solving Skills

Learning Python not only teaches you how to code but also helps you develop critical thinking and problem-solving skills. These skills are transferable across various domains, making Python a valuable tool for both technical and non-technical roles.

10. Future-Proof Your Career

Python’s growth trajectory shows no signs of slowing down. Its widespread adoption in emerging fields like AI, machine learning, and blockchain ensures that Python will remain relevant for years to come. By learning Python, you’re investing in a skill that will keep you ahead in the tech landscape.

Conclusion

Python is more than just a programming language; it’s a gateway to endless possibilities. Whether you aspire to develop cutting-edge applications, analyze data, or automate tasks, Python offers the tools and community to help you succeed. Start your Python journey today and unlock a world of opportunities in technology and beyond.

[xaltius]

A 5-Step Data Science Guide Anyone Can Follow

Data science has become a cornerstone of modern business and technological advancements. It's the art of extracting valuable insights from data, enabling data-driven decisions that can revolutionize industries. If you're intrigued by the world of data science, here's a 5-step guide to help you embark on your data science journey:

Step 1: Build a Strong Foundation in Mathematics and Statistics

Probability and Statistics: Understand probability distributions, hypothesis testing, and statistical inference.

Linear Algebra: Grasp concepts like matrices, vectors, and linear transformations.

Calculus: Learn differential and integral calculus to understand optimization techniques.

Step 2: Master Programming Languages

Python: A versatile language widely used in data science for data manipulation, analysis, and machine learning.

R: A statistical programming language specifically designed for data analysis and visualization.

SQL: Master SQL to interact with databases and extract relevant data.

Step 3: Dive into Data Analysis and Visualization

Pandas and NumPy: Python libraries for data manipulation and analysis.

Matplotlib and Seaborn: Python libraries for data visualization.

Tableau and Power BI: Powerful tools for creating interactive data visualizations.

Step 4: Learn Machine Learning

Supervised Learning: Understand algorithms like linear regression, logistic regression, decision trees, and random forests.

Unsupervised Learning: Explore techniques like clustering, dimensionality reduction, and anomaly detection.

Deep Learning: Learn about neural networks and their applications in various domains.

Step 5: Gain Practical Experience

Personal Projects: Work on data science projects to apply your skills and build a portfolio.

Kaggle Competitions: Participate in data science competitions to learn from others and improve your skills.

Internships and Co-ops: Gain hands-on experience in a real-world setting.

Remember, data science is a continuous learning process. Stay updated with the latest trends and technologies by following blogs, attending conferences, and participating in online communities.

Xaltius Academy offers comprehensive data science training programs to equip you with the skills and knowledge needed to excel in this growing field. Our expert instructors and hands-on labs will prepare you for success in your data science career.

By following these steps and staying committed to learning, you can embark on a rewarding career in data science.

[the-proeducator-blog]

Why Study Information Technology in the UK? Key Benefits and Opportunities

Are you considering pursuing a degree in Information Technology (IT) and wondering if the UK is the right place for you? The UK is home to some of the best universities for IT education, offering world-class programs that cater to various interests within the tech world. From data science to artificial intelligence (AI), the opportunities to learn and grow in this field are endless.

To find out more about the best universities for Information Technology UK, and explore your options for an IT career that could open doors to exciting global opportunities.

Top Reasons to Study Information Technology in the UK

The decision to study Information Technology in the UK comes with numerous advantages that can set you on the path to a successful career. Here’s why the UK is one of the best destinations for IT students:

1. High-Quality Education

UK universities are recognized worldwide for their academic excellence, offering cutting-edge IT programs that blend theory and practical experience. These universities use state-of-the-art technology in their curricula and maintain strong links with industry, ensuring that students gain the skills employers are looking for.

2. World-Class Universities and Programs

The UK is home to some of the most renowned universities globally, offering specialized IT programs in fields such as Software Engineering, Data Science, Cybersecurity, Artificial Intelligence (AI), and more. Studying in these universities not only guarantees a top-tier education but also connects you with a global network of professionals.

To learn more about the best universities for Information Technology UK, and explore which institution aligns best with your career goals.

3. Strong Industry Connections and Networking Opportunities

Studying in the UK means you’ll have access to one of the most vibrant tech industries in the world. Many UK universities collaborate closely with major tech companies, offering internships, industrial placements, and research opportunities. As a student, you’ll be able to gain practical experience and network with professionals, giving you a head start in your career after graduation.

Diverse Career Opportunities After IT Studies in the UK

Graduating with an IT degree from a UK university opens doors to a wide range of career opportunities. The IT industry is one of the fastest-growing sectors worldwide, and the UK’s tech industry is no exception. Whether you’re interested in becoming a software developer, data scientist, or cybersecurity expert, the demand for IT professionals is consistently high.

1. Access to High Paying Jobs

The UK is home to major global companies such as Google, Facebook, and Microsoft, along with numerous start-ups and tech firms. As an IT graduate, you’ll have access to a wealth of job opportunities with competitive salaries. The UK’s thriving tech industry, along with its emphasis on innovation, ensures that IT professionals are highly sought after.

2. Global Recognition of UK Degrees

Degrees from UK universities are highly regarded worldwide. Employers across the globe recognize the quality of education you receive in the UK, which enhances your employability no matter where you want to work. Whether you’re planning to stay in the UK, return to your home country, or work internationally, a degree from a UK institution adds credibility to your CV.

3. Cultural Diversity and International Student Community

Studying in the UK offers an enriching cultural experience, with students from all over the world coming together. This diversity allows you to gain new perspectives, learn about different cultures, and make lasting connections with people from various backgrounds. This multicultural environment can be particularly valuable if you plan to work in international IT teams or companies.

What Makes IT Education in the UK Unique?

1. Cutting-Edge Research and Innovation

UK universities lead the way in IT research and development. With their focus on emerging technologies such as AI, machine learning, and blockchain, students have the opportunity to engage in groundbreaking research. Many UK universities partner with industry leaders to bring the latest technologies into their curriculum, giving students the chance to work on real-world problems and innovate.

2. Flexible Course Options

UK universities offer a variety of course options for IT students, including undergraduate, postgraduate, and PhD programs. Whether you’re looking for a general IT degree or a specialized course, you’ll find plenty of options to tailor your education to your interests and career goals.

Conclusion: Is Studying Information Technology in the UK Right for You?

If you’re passionate about technology and eager to pursue a career in IT, studying in the UK could be one of the best decisions you make. With top-ranked universities, strong industry connections, and a thriving job market, the UK offers everything you need to succeed in the tech industry.

To explore the best universities for Information Technology UK, and get insights on the best IT programs.

Whether you're interested in software development, data science, cybersecurity, or AI, the UK provides an outstanding environment to kick-start your IT career. Get ready for a future full of opportunities in the ever-growing tech world!

[cybromtechnology2]

Join top-notch Artificial Intelligence and Data Science courses at Cybrom Technology. Expert-led data science and machine learning coaching near you in Bhopal. Enroll now to upskill

[codedusoftware]

How Custom Software Development Transforms Modern Businesses: Insights from CodEduIn an era dominated by rapid technological advancements, businesses are under immense pressure to stay competitive, efficient, and customer-focused. Off-the-shelf software, while useful, often falls short in addressing the unique challenges and dynamic needs of individual businesses. This is where custom software development steps in—a solution tailored specifically to meet the requirements of a business.

CodEdu Software Technologies, based in Cochin, Kerala, specializes in creating innovative, customer-centric software solutions that empower businesses to streamline operations, improve productivity, and enhance customer experiences. In this blog, we’ll explore how custom software development is transforming modern businesses and why partnering with CodEdu can be a game-changer.

What Is Custom Software Development? Custom software development involves designing, developing, and deploying software solutions tailored to meet a business's specific requirements. Unlike generic, off-the-shelf software, custom solutions are built from the ground up to align with a company’s processes, goals, and challenges.

This personalized approach allows businesses to create tools that integrate seamlessly with their existing operations, enhancing efficiency and providing a competitive edge.

The Key Benefits of Custom Software Development

Tailored to Specific Business Needs Custom software is designed to address a company’s unique requirements. Whether it’s automating a workflow, integrating with other tools, or solving specific challenges, the solution is built to fit seamlessly into the business ecosystem.

For example, an e-commerce business may require a software system that combines inventory management, personalized customer recommendations, and a secure payment gateway. Off-the-shelf software may provide one or two of these features but rarely all in an integrated manner.

Enhanced Efficiency and Productivity Custom software eliminates redundancies and streamlines operations. By automating repetitive tasks and integrating seamlessly with existing tools, businesses can significantly reduce manual effort and focus on core activities.

CodEdu has worked with several businesses to create custom solutions that enhance efficiency. One notable example is a manufacturing client who needed real-time tracking of production cycles. The tailored solution reduced delays and optimized resource allocation, saving the client both time and money.

Scalability for Future Growth One of the major limitations of off-the-shelf software is its inability to scale. As businesses grow and evolve, their software needs change. Custom software, on the other hand, is designed with scalability in mind.

CodEdu’s solutions are built to grow alongside businesses, allowing for easy updates and additional features as new challenges and opportunities arise.

Improved Security Data security is a top concern for businesses today. Custom software allows for the integration of advanced security features tailored to the specific vulnerabilities of the organization.

Unlike generic solutions that use standard security protocols, custom software incorporates unique safeguards, making it harder for malicious actors to breach the system.

Cost-Effectiveness in the Long Run While the initial investment for custom software may be higher than purchasing off-the-shelf solutions, it offers significant savings in the long run. Businesses avoid recurring licensing fees, third-party tool integration costs, and inefficiencies caused by mismatched software capabilities.

Real-World Applications of Custom Software Development Custom software development is revolutionizing industries by offering solutions that address specific operational challenges. Here are some examples of how businesses are leveraging tailored solutions:

E-Commerce Industry E-commerce companies face unique challenges, such as managing large inventories, providing personalized customer experiences, and ensuring secure transactions. Custom software can integrate inventory management systems, CRM tools, and AI-driven recommendation engines into a single platform, streamlining operations and boosting sales.

Healthcare Sector The healthcare industry requires solutions that ensure patient confidentiality, streamline appointment scheduling, and manage medical records efficiently. Custom software allows healthcare providers to deliver telemedicine services, maintain compliance with industry regulations, and improve patient outcomes.

Education and Training Educational institutions and training academies are leveraging custom Learning Management Systems (LMS) to provide personalized learning experiences. CodEdu has developed platforms that enable online assessments, real-time feedback, and interactive learning tools for students.

Logistics and Supply Chain Logistics companies require software that provides real-time tracking, route optimization, and automated billing. CodEdu has partnered with logistics providers to build solutions that reduce operational costs and enhance customer satisfaction.

How CodEdu Approaches Custom Software Development At CodEdu Software Technologies, we believe in a collaborative, customer-centric approach to software development. Here’s how we ensure the delivery of high-quality solutions:

Understanding Business Needs Our process begins with a detailed consultation to understand the client’s goals, pain points, and operational workflows. This ensures that the solution aligns perfectly with the business’s requirements.

Agile Development Methodology We adopt an agile approach to development, breaking the project into smaller, manageable phases. This allows for flexibility, regular feedback, and timely delivery of the final product.

Cutting-Edge Technology Our team leverages the latest technologies, including AI, machine learning, cloud computing, and blockchain, to deliver innovative and robust solutions.

Ongoing Support and Maintenance Software development doesn’t end with deployment. We provide ongoing support and updates to ensure the solution remains effective as the business evolves.

Future Trends in Custom Software Development The world of custom software development is continuously evolving. Here are some trends that are shaping the future:

AI and Machine Learning Integration Artificial Intelligence (AI) and machine learning are enabling businesses to automate processes, predict trends, and provide personalized customer experiences. From chatbots to predictive analytics, these technologies are transforming industries.

Cloud-Based Solutions Cloud computing is revolutionizing software development by offering scalability, accessibility, and cost efficiency. Businesses are increasingly adopting cloud-based custom software to enable remote access and collaboration.

IoT-Driven Solutions The Internet of Things (IoT) is creating opportunities for custom software that connects devices and collects data in real-time. This is particularly beneficial in industries such as healthcare, logistics, and manufacturing.

Low-Code and No-Code Platforms Low-code and no-code platforms are simplifying the development process, allowing businesses to create custom software with minimal technical expertise. While not a replacement for traditional development, these platforms are enabling faster prototyping and iteration.

Why Choose CodEdu for Custom Software Development? CodEdu Software Technologies stands out as a trusted partner for custom software development. Here’s why:

Experienced Team: Our developers bring years of experience in crafting innovative solutions for diverse industries. Customer-Centric Approach: We prioritize your business goals, ensuring the software delivers real value. Proven Track Record: With a portfolio of successful projects, CodEdu has earned a reputation for delivering quality and reliability. End-to-End Services: From consultation to development and post-deployment support, we handle every aspect of the project. Conclusion Custom software development is no longer an option but a necessity for businesses aiming to stay competitive in today’s digital landscape. It empowers organizations to streamline operations, enhance security, and deliver exceptional customer experiences.

CodEdu Software Technologies, with its expertise in innovation and customer-centric solutions, is the ideal partner to help businesses harness the power of custom software. Whether you’re a startup looking to establish a strong foundation or an established enterprise aiming to optimize operations, our tailored solutions can drive your success.

Ready to transform your business? Contact CodEdu Software Technologies today and let’s build the future together.

[perfectbouquetcollection]

Enroll in a comprehensive Data Science and AI course to gain expertise in machine learning, deep learning, data analysis, and more. Learn from top instructors with real-world projects and career support!

[jcmarchi]

Teaching a robot its limits, to complete open-ended tasks safely

New Post has been published on https://thedigitalinsider.com/teaching-a-robot-its-limits-to-complete-open-ended-tasks-safely/

Teaching a robot its limits, to complete open-ended tasks safely

If someone advises you to “know your limits,” they’re likely suggesting you do things like exercise in moderation. To a robot, though, the motto represents learning constraints, or limitations of a specific task within the machine’s environment, to do chores safely and correctly.

For instance, imagine asking a robot to clean your kitchen when it doesn’t understand the physics of its surroundings. How can the machine generate a practical multistep plan to ensure the room is spotless? Large language models (LLMs) can get them close, but if the model is only trained on text, it’s likely to miss out on key specifics about the robot’s physical constraints, like how far it can reach or whether there are nearby obstacles to avoid. Stick to LLMs alone, and you’re likely to end up cleaning pasta stains out of your floorboards.

To guide robots in executing these open-ended tasks, researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) used vision models to see what’s near the machine and model its constraints. The team’s strategy involves an LLM sketching up a plan that’s checked in a simulator to ensure it’s safe and realistic. If that sequence of actions is infeasible, the language model will generate a new plan, until it arrives at one that the robot can execute.

This trial-and-error method, which the researchers call “Planning for Robots via Code for Continuous Constraint Satisfaction” (PRoC3S), tests long-horizon plans to ensure they satisfy all constraints, and enables a robot to perform such diverse tasks as writing individual letters, drawing a star, and sorting and placing blocks in different positions. In the future, PRoC3S could help robots complete more intricate chores in dynamic environments like houses, where they may be prompted to do a general chore composed of many steps (like “make me breakfast”).

“LLMs and classical robotics systems like task and motion planners can’t execute these kinds of tasks on their own, but together, their synergy makes open-ended problem-solving possible,” says PhD student Nishanth Kumar SM ’24, co-lead author of a new paper about PRoC3S. “We’re creating a simulation on-the-fly of what’s around the robot and trying out many possible action plans. Vision models help us create a very realistic digital world that enables the robot to reason about feasible actions for each step of a long-horizon plan.”

The team’s work was presented this past month in a paper shown at the Conference on Robot Learning (CoRL) in Munich, Germany.

Play video

Teaching a robot its limits for open-ended chores MIT CSAIL

The researchers’ method uses an LLM pre-trained on text from across the internet. Before asking PRoC3S to do a task, the team provided their language model with a sample task (like drawing a square) that’s related to the target one (drawing a star). The sample task includes a description of the activity, a long-horizon plan, and relevant details about the robot’s environment.

But how did these plans fare in practice? In simulations, PRoC3S successfully drew stars and letters eight out of 10 times each. It also could stack digital blocks in pyramids and lines, and place items with accuracy, like fruits on a plate. Across each of these digital demos, the CSAIL method completed the requested task more consistently than comparable approaches like “LLM3” and “Code as Policies”.

The CSAIL engineers next brought their approach to the real world. Their method developed and executed plans on a robotic arm, teaching it to put blocks in straight lines. PRoC3S also enabled the machine to place blue and red blocks into matching bowls and move all objects near the center of a table.

Kumar and co-lead author Aidan Curtis SM ’23, who’s also a PhD student working in CSAIL, say these findings indicate how an LLM can develop safer plans that humans can trust to work in practice. The researchers envision a home robot that can be given a more general request (like “bring me some chips”) and reliably figure out the specific steps needed to execute it. PRoC3S could help a robot test out plans in an identical digital environment to find a working course of action — and more importantly, bring you a tasty snack.

For future work, the researchers aim to improve results using a more advanced physics simulator and to expand to more elaborate longer-horizon tasks via more scalable data-search techniques. Moreover, they plan to apply PRoC3S to mobile robots such as a quadruped for tasks that include walking and scanning surroundings.

“Using foundation models like ChatGPT to control robot actions can lead to unsafe or incorrect behaviors due to hallucinations,” says The AI Institute researcher Eric Rosen, who isn’t involved in the research. “PRoC3S tackles this issue by leveraging foundation models for high-level task guidance, while employing AI techniques that explicitly reason about the world to ensure verifiably safe and correct actions. This combination of planning-based and data-driven approaches may be key to developing robots capable of understanding and reliably performing a broader range of tasks than currently possible.”

Kumar and Curtis’ co-authors are also CSAIL affiliates: MIT undergraduate researcher Jing Cao and MIT Department of Electrical Engineering and Computer Science professors Leslie Pack Kaelbling and Tomás Lozano-Pérez. Their work was supported, in part, by the National Science Foundation, the Air Force Office of Scientific Research, the Office of Naval Research, the Army Research Office, MIT Quest for Intelligence, and The AI Institute.

[drax0001]

How to Prepare for Data Analytics Interviews

Preparing for a data analytics interview can feel overwhelming, but with the right approach, you can ace it. Here are some practical tips to help you succeed:

1. Understand the Role

Read the job description carefully to know the skills and tools required.

Research the company’s industry, products, and services to tailor your responses.

2. Master the Fundamentals

Brush up on statistics, probability, and data visualization principles.

Be clear on concepts like correlation vs. causation, regression analysis, and hypothesis testing.

3. Get Hands-On with Tools

Practice using tools like Excel, SQL, Python, R, or Tableau.

Be ready to demonstrate your ability to manipulate and analyze data effectively.

4. Prepare for Technical Questions

Expect questions on SQL queries, data wrangling, and ETL processes.

Practice coding challenges on platforms like HackerRank or LeetCode.

5. Work on Business Acumen

Understand how data drives decision-making in businesses.

Practice interpreting data in the context of business goals and challenges.

6. Review Your Past Projects

Be prepared to discuss your past projects in detail.

Highlight the problem, your approach, tools used, and the impact of your analysis.

7. Focus on Soft Skills

Develop the ability to explain complex data insights to non-technical stakeholders.

Practice answering behavioral questions using the STAR method (Situation, Task, Action, Result).

8. Prepare for Case Studies

Practice solving case studies or real-world data problems.

Structure your response logically and explain your thought process.

9. Stay Updated

Keep up with industry trends, emerging tools, and best practices in data analytics.

Read blogs, attend webinars, or take short courses to stay current.

10. Mock Interviews

Participate in mock interviews with peers or mentors.

Get feedback to improve your responses and build confidence.

By focusing on these areas and practicing consistently, you’ll increase your chances of success in data analytics interviews. Good luck! 🚀

Would you like me to expand on any of these points?

[xaltius]

How to Get into Data Science: A Comprehensive Guide

Data science has emerged as one of the most sought-after fields in today's technology-driven world. With the increasing volume of data generated every day, organizations are looking for skilled data scientists to extract valuable insights. If you're interested in a career in data science, here's a comprehensive guide to help you get started:

1. Build a Strong Foundation in Mathematics and Statistics:

Probability and Statistics: Understand probability distributions, hypothesis testing, and statistical inference.

Linear Algebra: Grasp concepts like matrices, vectors, and linear transformations.

Calculus: Learn differential and integral calculus to understand optimization techniques.

2. Learn Programming Languages:

Python: A versatile language widely used in data science for data manipulation, analysis, and machine learning.

R: A statistical programming language specifically designed for data analysis and visualization.

SQL: Master SQL to interact with databases and extract relevant data.

3. Master Data Analysis and Visualization Tools:

Pandas and NumPy: Python libraries for data manipulation and analysis.

Matplotlib and Seaborn: Python libraries for data visualization.

Tableau and Power BI: Powerful tools for creating interactive data visualizations.

4. Dive into Machine Learning:

Supervised Learning: Understand algorithms like linear regression, logistic regression, decision trees, and random forests.

Unsupervised Learning: Explore techniques like clustering, dimensionality reduction, and anomaly detection.

Deep Learning: Learn about neural networks and their applications in various domains.

5. Gain Practical Experience:

Personal Projects: Work on data science projects to apply your skills and build a portfolio.

Kaggle Competitions: Participate in data science competitions to learn from others and improve your skills.

Internships and Co-ops: Gain hands-on experience in a real-world setting.

6. Continuous Learning:

Online Courses and Certifications: Enroll in online courses on platforms like Coursera, edX, and Udemy. Consider certifications like Data Science Certified Professional (DSCP) or Certified Data Scientist (CDS).

Stay Updated: Keep up with the latest trends and advancements in data science by following blogs, attending conferences, and participating in online communities.

Xaltius Academy offers comprehensive data science training programs to equip you with the skills and knowledge needed to excel in this growing field. Our expert instructors and hands-on labs will prepare you for success in your data science career.

[online-programmes]

LIBA’s Online Post Graduate Programme in Management (PGPM)

Brief of the programme:

LIBA offers a 11 month Online PGPM combining core management and specialized courses. The programme is for students who wanted to learn about the foundation of business and for working professionals who wanted to develop their knowledge further.

Curriculum

As mentioned above there are Core as well as Specialized subjects. Core management subjects include Principles of management, Quantitative studies, Marketing, Economics, Finance, Management Information system and more. Specialization courses are designed to gain knowledge about the in- depth aspects of the course. This PGPM programme offers three specialization courses from which the students can choose one. Specialization courses are AI & Data Science, Healthcare and FinTech.

Who can apply for this programme?

Applicants who hold a bachelor’s degree with a minimum aggregate of 50% marks. Also those who have a minimum of 2 years full - time work experience are preferred but it's not mandatory.

Individuals who seek further knowledge in business, those who want to learn about the fundamental aspects of management, professionals who want a to achieve more in their career, people who want to transition from their job to AI & Data science or Finance or Healthcare, Technology and Healthcare professionals who wanted to strengthen their competencies, individuals who wanted to foster their innovative thinking in technology and finance can apply for this programme.

Why should you apply?

With respect to the specializations offered, all 3 course subjects are vital in today’s world. AI & Data Science deals with machine learning algorithms, big data analytics and cutting- edge AI applications. Since the world is getting accustomed to AI, there is a growing demand for this course and for people who complete it.

FinTech specialization includes financial technology, blockchain, digital payments and financial as well as data analytics. This course also includes practical projects, industry insights, and collaborations with financial institutions.

Healthcare Specialization emphasises on predictive analytics, personalized medicine, health informatics, policy, economics, information systems along with training individuals through data-driven insights and technological advancements.

Apart from that, there are various reasons as to why one should apply for this course. Primary reason would be to develop and deepen your knowledge. If you are a student, you will learn about the basics of the subject. If you are a working professional, you will be able to gain wisdom to have career advancement in your domain. You will be able to connect with distinguished people while you are enrolled in the course.

In this world, having knowledge in theory alone doesn’t help, we should know how to apply the theoretical knowledge in the real world. With regards to that, this programme trains you to achieve it by providing different case studies, projects and assignments. There are live sessions and self- paced modules to make the learning process convenient and exciting. Ample resources are given access to refer and study. Courses are taught by renowned faculty who are field specialists and experienced scholars. Finally, at the end of this course you would’ve developed both personal attributes and job-related skills.

Why wait? Initiate your learning journey now -

https://iop.liba.edu/pg-program-in-healthcare-management/

Learn more about:

AI & Data science specialization - https://iop.liba.edu/ai-pgpm/

Fintech specialization - https://iop.liba.edu/fs-pgpm/

Healthcare specialization - https://iop.liba.edu/hs-pgpm/

[sanjanabia]

From Chatbots to Autonomous Systems: How AI is Evolving Through Reinforcement Learning

Artificial Intelligence (AI) is revolutionizing numerous industries, and one of the most significant advancements driving this evolution is reinforcement learning (RL). This dynamic branch of machine learning focuses on how agents can learn optimal behaviors through trial and error by interacting with their environments. From enhancing chatbots to enabling autonomous systems, reinforcement learning is at the forefront of AI innovation. For those interested in mastering these concepts, enrolling in a data analytics course in Kolkata can provide the essential skills needed to understand and apply RL techniques effectively.

What is Reinforcement Learning?

Reinforcement learning is a learning paradigm where agents learn to make decisions by performing actions in an environment to achieve specific goals. Unlike traditional machine learning methods that rely on labeled data, RL uses a reward system to encourage desired behaviors. Here’s how it works:

Agent: The learner or decision-maker.

Environment: The context within which the agent operates.

Actions: The choices available to the agent.

Rewards: Feedback from the environment that evaluates the success of the agent’s actions.

Through this feedback loop, agents learn to maximize their cumulative rewards, making RL particularly suited for complex decision-making tasks.

Applications of Reinforcement Learning

Reinforcement learning has a wide range of applications that demonstrate its capabilities. Two notable areas where RL is making significant impacts are chatbots and autonomous systems.

Chatbots:

Personalization: Modern chatbots use RL to improve user interactions by learning from past conversations. This allows them to adapt their responses based on user preferences and behavior.

Efficiency: By optimizing conversation pathways, RL enables chatbots to provide faster and more accurate responses, enhancing user satisfaction.

Learning from Feedback: Chatbots can adjust their strategies in real-time, learning which types of responses yield the best outcomes in terms of user engagement.

Autonomous Systems:

Robotics: In robotics, RL empowers machines to navigate environments, making real-time decisions to avoid obstacles and accomplish tasks. This is crucial for applications in warehouses, factories, and even space exploration.

Self-Driving Cars: Autonomous vehicles rely on RL to make split-second decisions based on sensory data. By continuously learning from driving experiences, these systems become safer and more efficient.

Game Playing: RL has gained fame through its success in games like Go and chess, where agents learn to play at superhuman levels by exploring vast action spaces and optimizing strategies.

The Importance of Data Analytics in Reinforcement Learning

Understanding reinforcement learning requires a solid foundation in data analytics, which is where a data analytics course in Kolkata can be beneficial. Here are some key areas covered in such a course that are directly applicable to RL:

Data Preprocessing: Preparing data for analysis is essential in RL, as it often involves large and complex datasets.

Statistical Analysis: Understanding the principles of statistics is crucial for interpreting reward signals and evaluating agent performance.

Machine Learning Algorithms: A solid grasp of various machine learning techniques is necessary for implementing RL algorithms effectively.

Model Evaluation: Learning how to assess the performance of RL models is vital for improving their effectiveness and reliability.

By acquiring these skills through a data analytics course in Kolkata, individuals can position themselves at the cutting edge of AI technology.

Challenges in Reinforcement Learning

While reinforcement learning offers tremendous potential, it also faces several challenges:

Sample Efficiency: RL often requires a vast amount of data to learn effectively, making it resource-intensive.

Stability and Convergence: Ensuring that RL algorithms converge to optimal solutions can be complex, particularly in dynamic environments.

Exploration vs. Exploitation: Balancing the need to explore new strategies while exploiting known successful ones is a critical aspect of RL that can affect learning outcomes.

Addressing these challenges is crucial for advancing the field and enabling RL applications in real-world scenarios.

The Future of Reinforcement Learning

The future of reinforcement learning is promising, with ongoing research aimed at overcoming current limitations and expanding its applications. As industries increasingly adopt AI technologies, the integration of RL into more complex systems will likely lead to breakthroughs in automation, personalization, and efficiency.

For individuals eager to be part of this evolving landscape, enrolling in a data analytics course in Kolkata can provide the necessary training to understand and implement reinforcement learning techniques. This education can open doors to exciting career opportunities in AI and data science.

Conclusion

Reinforcement learning is a game-changing technology that is reshaping the landscape of artificial intelligence. From enhancing chatbots to powering autonomous systems, its applications are vast and impactful. As the field continues to evolve, understanding the principles of RL through a data analytics course in Kolkata becomes increasingly valuable. With the right skills and knowledge, individuals can contribute to the advancements in AI and play a pivotal role in the future of technology. Embracing reinforcement learning not only enhances career prospects but also fosters innovation across various industries.