Meta Unveils Latest AI Model: Llama 4

 

Meta Platforms announced on Saturday the release of its latest large language models, Llama 4 Scout and Llama 4 Maverick, describing them as its “most advanced models yet” and “the best in their class for multimodality.” These models are part of Meta’s push into multimodal AI, systems capable of understanding and generating content across text, images, video, and audio.

Both Llama 4 Scout and Maverick will be available as open-source software, signaling Meta’s continued commitment to collaborative AI development. In addition, the company offered a preview of Llama 4 Behemoth, a powerful upcoming model it calls “one of the smartest LLMs in the world,” designed to serve as a teacher for future systems.

The release follows reports that Meta had delayed the launch of Llama 4 due to the models falling short of internal benchmarks in reasoning and math tasks. Concerns also arose over their performance in humanlike voice conversations compared to competitors like OpenAI.

As part of its broader AI ambitions, Meta plans to invest up to $65 billion this year in AI infrastructure, responding to growing investor pressure and intensifying competition in the AI space sparked by the success of AI tools.

The Future of Robotics: Careers and How to Get Started

 

Robotics is one of the most exciting fields of the future—it brings together AI, hardware, engineering, and imagination. Let’s talk about where it’s going, what careers are opening up, and how to get started, especially if you're new to it.

The Future of Robotics

1. Where Robotics Is Heading

• Everyday helpers: Home robots (like smart vacuums, personal assistants, elder care bots).
• Healthcare robots: Surgical robots, exoskeletons, and hospital automation.
• Industrial automation: Smart factories with robotic arms and autonomous logistics.
• Exploration: Space robots (Mars rovers, lunar missions), underwater bots.
• Military & disaster response: Drones, bomb disposal units, rescue bots.
• Humanoid robots: Like Tesla's Optimus or Boston Dynamics’ Atlas—early days, but promising.

Think: a world where robots assist, enhance, or fully automate complex human tasks.

💼 Career Paths in Robotics

Robotics is super interdisciplinary. You can go into:

Career Path	What You Do	Skills Needed
Robotics Engineer	Design/build robots	Mechanical + electrical + software
AI/ML Engineer	Teach robots to "think"	Python, machine learning, computer vision
Embedded Systems Engineer	Program robot hardware	C/C++, firmware, microcontrollers
Control Systems Engineer	Motion control and feedback systems	Math, physics, signal processing
Mechanical Engineer	Robot design, structure	CAD, kinematics, materials science
Robotics Technician	Maintain/build systems	Hands-on hardware/electronics skills
UX/Interaction Designer	How humans use robots	Psychology, HCI, design thinking

You can also blend robotics with fields like:

• Medicine (robotic surgery)
• Agriculture (harvesting bots)
• Education (robot kits for kids)
• Entertainment (robotic animation, theme parks)

 How to Get Started in Robotics

 1. Start Learning the Basics

• Programming: Python, C++, or even Arduino/C.
• Electronics: Learn circuits, sensors, microcontrollers (like Raspberry Pi or Arduino).
• Math & Physics: Linear algebra, control systems, mechanics.

2. Free Online Resources

• MIT OpenCourseWare – Introduction to Robotics
• Coursera – Robotics Specialization
• [EdX, Udemy, or YouTube channels like "Robotics with Python" or "Jeremy Fielding" for hands-on builds]

3. Get Hands-On

• Build simple robots at home: Line follower, robotic arm, obstacle avoider.
• Join a robotics club or hackathon.
• Get a beginner kit: Arduino starter kit, Lego Mindstorms, or Raspberry Pi.

4. Practice Projects to Try

• Self-driving car with sensors.
• Face-tracking robot using computer vision.
• Robotic arm controlled with a joystick.
• Mini delivery bot with wheels.

5. Internships & Competitions

• Look for robotics internships, Maker Faires, or join FIRST Robotics, RoboCup, or BattleBots events.

Skills That Will Set You Apart

• Problem-solving & creativity
• Teamwork and communication (robots are rarely a one-person job)
• Cross-domain knowledge: Blend mechanics, code, and design
• Persistence—robots will break, and you’ll learn a lot from fixing them

Where the Jobs Are

• Companies like Boston Dynamics, Meta, Tesla, ABB, iRobot, DJI, Intuitive Surgical, NVIDIA, and even NASA.
• Robotics startups are booming in fields like logistics, agriculture, and home automation.
• Or… build your own robot company

Salary of Robotics engineer

The salary of a Robotics Engineer can be very rewarding, especially as the field grows rapidly across industries like tech, aerospace, healthcare, and manufacturing.

Here’s a breakdown by experience, location, and industry:

💵 Average Salary (2025 Estimates)

🌍 Global Average

• Entry-Level (0–2 years): $60,000 – $85,000 per year
• Mid-Level (3–6 years): $85,000 – $120,000
• Senior/Lead (7+ years): $120,000 – $160,000+
• Top Companies / Specialized Roles: $180K – $250K+ (especially with AI or autonomy expertise)

🇺🇸 United States (USD)

• National Average: ~$100,000 – $110,000
• High-paying areas:
• Silicon Valley (CA): $120K – $160K
• Boston, MA (robotics research hub): $110K – $140K
• Seattle, TX, CO: Also strong tech centers

🇮🇳 India (INR)

• Entry-Level: ₹5–10 LPA
• Mid-Level: ₹10–20 LPA
• Senior Roles: ₹20–40+ LPA
• Big companies like Tata Elxsi, ABB, and global robotics firms pay better with experience.

🇬🇧 UK

• Entry-Level: £28,000 – £40,000
• Mid-Level: £40,000 – £60,000
• Senior: £60,000 – £90,000+

Which Industries Pay More?

Industry	Pay Level
Aerospace / Defense (e.g., Lockheed, SpaceX)	🔥 High
AI & Autonomous Vehicles (e.g., Tesla, NVIDIA)	🔥 High
Healthcare Robotics (e.g., Intuitive Surgical)	🔥 High
Consumer Robotics (e.g., iRobot)	✅ Decent
Industrial Automation (e.g., ABB, Siemens)	✅ Stable
Research / Academia	❄️ Lower, but rewarding

---

Growth & Demand

• Robotics engineers are in high demand, especially those with AI/ML + robotics expertise.
• The rise of automation, smart factories, and service robots means more job openings every year.
• Jobs often blend robotics with computer vision, deep learning, and embedded systems—more skills = more pay.

The most talked-about tech entrepreneur of the time: Elon Musk

 

Elon Musk. Possibly the most talked-about tech entrepreneur of the time. Love him or hate him, the guy's fingerprints are all over the future. Let's break him down a bit:

⚡ Quick Snapshot of Elon Musk

• Born: 1971, South Africa
• Founder/CEO/Big Brain Behind:
• Tesla (Electric cars, energy)
• SpaceX (Rockets, Starlink, Mars dreams)
• Neuralink (Brain-machine interfaces)
• The Boring Company (Underground transport)
• X (formerly Twitter) (Social media)
• xAI (His AI company—rivaling OpenAI, Google, etc.)
• Was also a co-founder of PayPal

🚀 Why He Matters for the Future

1. Space Travel & Colonization

• SpaceX made rockets reusable. Huge cost savings.
• Plans to colonize Mars by the 2030s. (He says, “We must become a multi-planetary species.”)
• Starship (his big rocket) is being tested to carry cargo—and eventually, people—beyond Earth.

2. Revolutionizing Transportation

• Tesla led the EV revolution. Now every major car company is chasing them.
• Autonomous driving is still a work in progress, but he's pushing hard for AI-driven vehicles.
• Hyperloop and Boring Company aim to redefine how we move underground and between cities.

3. AI, Brains & Ethics

• He’s both excited and terrified of AI.
• Neuralink aims to merge humans with AI—brain implants that could help with memory loss, paralysis, and eventually human-AI symbiosis.
• Now running xAI, his own AI lab to create “truth-seeking AI.”

4. Social Media + Influence

• Took over Twitter/X in 2022 to promote “free speech”—led to a lot of chaos and reinvention.
• He's outspoken, polarizing, and basically posts memes one second, deep philosophy the next.

🤖 Criticisms & Controversy

• Accused of overpromising timelines (Mars by 2024? Not quite).
• Some worry about too much power in one person’s hands (space, internet, cars, brains, AI, media? That’s a lot).
• Has faced backlash for his social media behavior, company culture issues, and regulatory clashes.

🧠 Why People Pay Attention to Him

• He does things most people only talk about.
• He’s not afraid to take massive risks (almost went bankrupt multiple times).
• Even if he fails, he shifts the conversation around what’s possible.

🌌 Musk's Vision of the Future

• Humans on Mars
• AI as a helpful partner (not overlord)
• Global internet access
• Cleaner energy everywhere
• Mind-machine interfaces
• Faster-than-plane underground travel

 

The Future of Data Science: Careers and How to Get Started

 

Data Science in a Changing Landscape:

As industries increasingly rely on data-driven decision-making, the field of data science is evolving to meet new challenges and opportunities. Emerging technologies are transforming how data is collected, analyzed, and utilized, reshaping the skills and knowledge required for future professionals. Understanding these trends is crucial for those entering the field, as it will help guide their education and career paths.

In the coming years, significant advancements in artificial intelligence (AI), machine learning (ML), and quantum computing will redefine data processing and analytics. As these technologies evolve, data scientists must stay ahead of the curve. This article explores the key trends shaping data science, what aspiring professionals should anticipate, and how to embark on a successful career in the field.

Understanding Data Science:

Before exploring the future of data science, it's essential to understand its fundamental elements.

The Evolution of Data Science:

Data science has its roots in statistics and computer science, initially focusing on basic data processing and analysis. The 1960s and 1970s saw the rise of data warehousing and business intelligence. By the early 2000s, machine learning and AI became integral to data science, leading to sophisticated, data-driven decision-making. The explosion of big data in the past decade has further accelerated this evolution, making data science a crucial driver of innovation across industries such as finance, healthcare, and technology.

Core Technologies Driving Data Science:

Key components of data science include:

• Data Warehousing & Mining – The foundation of data storage and retrieval.

• Cloud Computing – Provides scalable, flexible data storage and processing solutions.

• Machine Learning & AI – Enables predictive analytics, automation, and intelligent decision-making.

• Data Visualization – Tools like Tableau and Power BI help translate complex data into actionable insights.

Preparing for a Career in Data Science: 

Aspiring data scientists should equip themselves with the right skills and resources to thrive in this rapidly evolving field.

Essential Skills for Beginners: 

To succeed in data science, developing a strong foundation in mathematics, statistics, and programming is essential. Key skills include:

• Programming Languages: Python, R, and SQL for data analysis and manipulation.

• Understanding of Algorithms & Data Structures: Critical for building machine learning models.

• Data Visualization & Cloud Computing: Provides a competitive edge in real-world applications.

Getting Started with Data Science Tools:

Popular tools like Jupyter Notebooks, TensorFlow, and Tableau are essential for data analysis and machine learning. Online platforms such as DataCamp and edX offer beginner-friendly courses, providing hands-on experience with key tools and techniques.

A Step-by-Step Guide to Becoming a Data Scientist:

Step 1: Master Data Analysis & Management:

Understanding data collection, cleaning, and organization is crucial. Learn SQL, Excel, and data warehousing concepts to develop strong foundational skills.

Step 2: Dive into Machine Learning & AI:

Build predictive models using algorithms like linear regression, decision trees, and neural networks. AI-powered automation is an essential skill in modern data science.

Step 3: Implement Big Data Solutions:

Gain experience with big data frameworks like Hadoop and Spark to handle large datasets efficiently. Understanding these technologies prepares you for data-intensive environments.

Step 4: Explore Advanced Analytics & Visualization:

Master advanced analytics techniques such as time-series analysis and geospatial visualization using tools like Tableau, Power BI, and D3.js.

Emerging Technologies Shaping Data Science:

The Impact of Quantum Computing:

Quantum computing has the potential to revolutionize data science by offering unprecedented computational power. Unlike traditional computers, quantum computers use qubits, enabling complex calculations at unimaginable speeds. This advancement could significantly enhance optimization problems in cryptography, financial modeling, and drug discovery.

Breakthroughs in Predictive Analytics:

The integration of deep learning techniques allows predictive analytics to process vast amounts of unstructured data, improving customer behavior forecasting, fraud detection, and risk management. Real-time data processing is also enhancing decision-making capabilities across industries.

Trends to Watch in Data Science:

Growing Importance of Data Ethics & Privacy:

With increased data collection, concerns over ethics and privacy are rising. Stricter regulations, such as GDPR and CCPA, require data scientists to ensure compliance and ethical data usage.

Changing Job Market Demands:

The job market is shifting towards candidates who possess technical expertise along with business acumen. The U.S. Bureau of Labor Statistics projects a 36% growth in data science roles between 2023 and 2033, making it one of the fastest-growing career fields.

Real-World Applications of Advanced Data Science:

Healthcare & Biotechnology Innovations:

Data science is revolutionizing healthcare through personalized medicine and AI-driven drug discovery. Companies like GNS Healthcare use machine learning to tailor treatments, while AstraZeneca leverages AI for faster drug development.

Financial Services Advancements:

Financial institutions utilize data science for fraud detection, risk assessment, and personalized services. JPMorgan Chase employs AI-driven algorithms to identify fraudulent transactions, while PayPal uses predictive analytics to enhance customer experiences.

Challenges in Data Science:

Addressing Data Security Concerns:

High-profile data breaches, such as the Facebook-Cambridge Analytica scandal, highlight the need for stringent data governance. Organizations are adopting encryption and regulatory compliance measures to enhance data security.

Bridging the Skills Gap in Emerging Technologies:

The rapid evolution of AI and quantum computing has created a skills gap in the workforce. Companies like IBM and Google are offering specialized training to address this challenge and equip professionals with advanced expertise.

Job Demand for Data Scientists:

The demand for data scientists remains strong worldwide in 2025. According to the World Economic Forum, data science and AI-related roles are among the fastest-growing careers globally. Businesses in various industries—such as finance, healthcare, technology, and e-commerce—are actively hiring data professionals to manage and analyze large datasets. Additionally, AI-driven analytics, cloud computing, and big data are further driving demand for skilled data scientists​.

Data Scientist Salaries by Country:

Data scientist salaries vary based on factors such as location, experience, specialization, and industry. Below are the estimated salaries for 2025:

• United States: $100,000 - $150,000 per year, with top professionals earning over $200,000, especially in tech hubs like San Francisco, New York, and Seattle​.

• United Kingdom: £60,000 - £90,000, with senior-level roles reaching over £100,000, particularly in London​.

• India: ₹6,00,000 - ₹20,00,000 for mid-level professionals, while senior data scientists (8+ years) can earn ₹30,00,000+ per year​.

• Germany: €50,000 - €90,000, with higher salaries in Berlin, Munich, and Frankfurt​.

• Dubai (UAE): AED 120,000 - AED 350,000 per year, depending on experience and industry​.

• Morocco: MAD 120,000 - MAD 250,000 per year, with the highest demand in telecom, finance, and government sectors​.

Factors Affecting Data Scientist Salaries:

• Specialization: Expertise in AI, machine learning (ML), deep learning, and cloud technologies (AWS, Azure, Google Cloud) significantly increase salaries.

• Industry: The finance, healthcare, and technology sectors offer the most lucrative salaries.

• Certifications & Skills: Certifications like Google’s Professional Data Engineer and Microsoft’s Azure Data Scientist boost employability​.

• Experience Level: Senior professionals (8+ years) often earn twice as much as entry-level data scientists.

Is Data Science Still a Good Career in 2025?

Yes! The field continues to grow, offering remote work opportunities, high salaries, and job security. However, continuous learning in AI, cloud computing, and big data analytics is essential to staying relevant.

Latest Developments in Artificial Intelligence of Bangladesh

 

1. AI Integration in Various Sectors

Bangladesh is making progress in AI adoption across industries such as telecom, finance, and retail. Companies like Intelligent Machines (IM) have successfully implemented AI solutions, improving business efficiency. For example, Unilever used AI-powered Fordo for precision marketing, achieving a 260% increase in performance, while bKash saw a 15% rise in monthly onboarding due to AI-powered automation. Additionally, Telenor reduced its KYC costs by 92.5% using AI-driven document verification tools​

2. Bangladesh’s AI Policy and Regulations

The Bangladesh government has drafted a National AI Policy that aims to guide AI development in 10 key sectors, including finance, education, and climate change. The policy emphasizes ethical AI use, data governance, and skill development, along with the establishment of a National AI Center of Excellence​

To regulate AI usage, the government is also working on the AI Act 2024, which is expected to be finalized by September 2025. The law aims to address AI risks while promoting innovation and aligning with the country's Smart Bangladesh Vision 2041​

3. Challenges in AI Adoption

Despite progress, Bangladesh faces challenges such as a lack of digital infrastructure, AI skill shortages, and limited data governance frameworks. The country's AI Preparedness Index score (0.38) is lower than that of regional competitors, indicating the need for faster workforce development and regulatory improvements​

4. Future Prospects

Experts believe that investing in AI education, research, and policy implementation can position Bangladesh as an AI-driven economy. Successful execution of AI strategies could enhance productivity, attract investments, and drive innovation​

The world is entering the era of wireless electricity !

 

Since the first electricity generation at the Pearl Power Station in New York in 1882, the only method of electricity supply has been through wired connections. However, in the current era, this method is not always effective. In many remote areas or in space, it is impossible to use wires for electricity supply. Additionally, due to climate change, the demand for electricity and its supply accessibility have become even more important. Therefore, scientists have been working for a long time on a new method of electricity supply without wires. This technology is called ‘Power Beaming.’ For over a century, scientists have been trying to transmit electricity through microwaves, radio waves, and lasers. The idea was first proposed by scientist Nikola Tesla, who suggested using the Earth's ionosphere to create a method of wireless electricity transmission. Although his research did not come to fruition, the pursuit of wireless electricity continued. After World War II, this technology began to develop further. In 1964, American engineer William C. Brown was able to power a small helicopter for 10 hours by transmitting electricity via microwaves. Later, in 1975, NASA scientist Richard Dickson successfully transmitted 30 kilowatts of electricity over a distance of one mile, though its efficiency was still not sufficient at the time.

However, due to advancements in technology, it is now on the path to becoming a reality. The progress in computers, photovoltaic technology, lasers, and transistors has made power beaming more effective. This technology is particularly gaining importance in reducing the use of fossil fuels. Currently, a New Zealand-based company, EMROD, is implementing a system that transmits electricity via microwaves directly from the power grid. Meanwhile, U.S.-based Reach Power is working on a method to supply electricity over a 25-kilometer range using radio waves. This technology is becoming increasingly important in the military sector and for commercial use. The United States, Europe, and Japan are investing heavily in this technology. Specifically, Japan’s space agency, JAXA, plans to implement wireless electricity supply from space by 2030. At present, this technology is being used for low-power applications, such as smart sensors, motion detectors, and smart displays in supermarkets. Gradually, it will be used on a larger scale. Scientists predict that this will soon become a reality, and in the future, wireless electricity will become an integral part of our daily lives.

Three professions that will remain resilient against AI automation​: Bill Gates

 

Bill Gates has identified three professions that he believes will remain resilient against AI automation: coders, biologists, and energy experts. While artificial intelligence is rapidly transforming industries and automating many jobs, Gates argues that these fields require human creativity, intuition, and decision-making that AI cannot fully replicate.

1. Coders – Despite AI’s growing ability to generate code, human programmers are still essential for debugging, refining algorithms, and improving AI models. Gates emphasizes that AI cannot self-improve without human oversight, making software developers integral to the technology's evolution​.

2. Biologists – AI can assist in analyzing medical data, diagnosing diseases, and scanning DNA, but it lacks the creativity and hypothesis-driven thinking required for scientific breakthroughs. Biologists will continue to lead innovation in medicine and life sciences, with AI serving as a supporting tool rather than a replacement​.

3. Energy Experts – The complexity of the energy sector, including grid management, regulatory challenges, and sustainable energy solutions, makes full automation impractical. Human oversight is critical for decision-making, crisis management, and long-term planning in the industry​.

Gates also pointed out that while AI will reshape the job market, the best way to stay competitive is to integrate AI into one’s work rather than resist it. He even predicted a shift toward shorter workweeks as automation takes over repetitive tasks, requiring professionals to focus on higher-level problem-solving.