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digitaleduskill · 1 month ago

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AI Research Methods: Designing and Evaluating Intelligent Systems

The field of artificial intelligence (AI) is evolving rapidly, and with it, the importance of understanding its core methodologies. Whether you're a beginner in tech or a researcher delving into machine learning, it’s essential to be familiar with the foundational artificial intelligence course subjects that shape the study and application of intelligent systems. These subjects provide the tools, frameworks, and scientific rigor needed to design, develop, and evaluate AI-driven technologies effectively.

What Are AI Research Methods?

AI research methods are the systematic approaches used to investigate and create intelligent systems. These methods allow researchers and developers to model intelligent behavior, simulate reasoning processes, and validate the performance of AI models.

Broadly, AI research spans across several domains, including natural language processing (NLP), computer vision, robotics, expert systems, and neural networks. The aim is not only to make systems smarter but also to ensure they are safe, ethical, and efficient in solving real-world problems.

Core Approaches in AI Research

1. Symbolic (Knowledge-Based) AI

This approach focuses on logic, rules, and knowledge representation. Researchers design systems that mimic human reasoning through formal logic. Expert systems like MYCIN, for example, use a rule-based framework to make medical diagnoses.

Symbolic AI is particularly useful in domains where rules are well-defined. However, it struggles in areas involving uncertainty or massive data inputs—challenges addressed more effectively by modern statistical methods.

2. Machine Learning

Machine learning (ML) is one of the most active research areas in AI. It involves algorithms that learn from data to make predictions or decisions without being explicitly programmed. Supervised learning, unsupervised learning, and reinforcement learning are key types of ML.

This approach thrives in pattern recognition tasks such as facial recognition, recommendation engines, and speech-to-text applications. It heavily relies on data availability and quality, making dataset design and preprocessing crucial research activities.

3. Neural Networks and Deep Learning

Deep learning uses multi-layered neural networks to model complex patterns and behaviors. It’s particularly effective for tasks like image recognition, voice synthesis, and language translation.

Research in this area explores architecture design (e.g., convolutional neural networks, transformers), optimization techniques, and scalability for real-world applications. Evaluation often involves benchmarking models on standard datasets and fine-tuning for specific tasks.

4. Evolutionary Algorithms

These methods take inspiration from biological evolution. Algorithms such as genetic programming or swarm intelligence evolve solutions to problems by selecting the best-performing candidates from a population.

AI researchers apply these techniques in optimization problems, game design, and robotics, where traditional programming struggles to adapt to dynamic environments.

5. Probabilistic Models

When systems must reason under uncertainty, probabilistic methods like Bayesian networks and Markov decision processes offer powerful frameworks. Researchers use these to create models that can weigh risks and make decisions in uncertain conditions, such as medical diagnostics or autonomous driving.

Designing Intelligent Systems

Designing an AI system requires careful consideration of the task, data, and objectives. The process typically includes:

Defining the Problem: What is the task? Classification, regression, decision-making, or language translation?

Choosing the Right Model: Depending on the problem type, researchers select symbolic models, neural networks, or hybrid systems.

Data Collection and Preparation: Good data is essential. Researchers clean, preprocess, and annotate data before feeding it into the model.

Training and Testing: The system learns from training data and is evaluated on unseen test data.

Evaluation Metrics: Accuracy, precision, recall, F1 score, or area under the curve (AUC) are commonly used to assess performance.

Iteration and Optimization: Models are tuned, retrained, and improved over time.

Evaluating AI Systems

Evaluating an AI system goes beyond just checking accuracy. Researchers must also consider:

Robustness: Does the system perform well under changing conditions?

Fairness: Are there biases in the predictions?

Explainability: Can humans understand how the system made a decision?

Efficiency: Does it meet performance standards in real-time settings?

Scalability: Can the system be applied to large-scale environments?

These factors are increasingly important as AI systems are integrated into critical industries like healthcare, finance, and security.

The Ethical Dimension

Modern AI research doesn’t operate in a vacuum. With powerful tools comes the responsibility to ensure ethical standards are met. Questions around data privacy, surveillance, algorithmic bias, and AI misuse have become central to contemporary research discussions.

Ethics are now embedded in many artificial intelligence course subjects, prompting students and professionals to consider societal impact alongside technical performance.

Conclusion

AI research methods offer a structured path to innovation, enabling us to build intelligent systems that can perceive, reason, and act. Whether you're designing a chatbot, developing a recommendation engine, or improving healthcare diagnostics, understanding these methods is crucial for success.

By exploring the artificial intelligence course subjects in depth, students and professionals alike gain the knowledge and tools necessary to contribute meaningfully to the future of AI. With a solid foundation, the possibilities are endless—limited only by imagination and ethical responsibility.

#ArtificialIntelligence #AI #MachineLearning #DeepLearning #AIResearch #IntelligentSystems #AIEducation #FutureOfAI #AIInnovation #DataScienc

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callofdutymobileindia · 2 months ago

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Artificial Intelligence Course in USA: A Complete Guide to Learning AI in 2025

Artificial Intelligence (AI) is transforming the world at an unprecedented pace, powering everything from personalized recommendations to self-driving vehicles. With industries across healthcare, finance, retail, and technology investing heavily in AI, there's never been a better time to upskill in this game-changing field. And when it comes to gaining a world-class education in AI, the United States stands as a global leader.

If you're considering enrolling in an Artificial Intelligence course in the USA, this guide will walk you through the benefits, curriculum, career prospects, and how to choose the right program.

What to Expect from an AI and ML Course in the USA?

The United States is a global leader in artificial intelligence and machine learning education, offering some of the most advanced academic and industry-aligned programs in the world. Whether you study at an Ivy League university, a top engineering school, or a specialized tech institute, AI and ML courses in the USA provide a deep, hands-on learning experience that prepares students for high-impact roles in both research and industry.

Strong Theoretical and Practical Foundations

AI and ML courses in the USA typically start by grounding students in essential concepts such as supervised and unsupervised learning, neural networks, deep learning, and probabilistic models. You’ll also study mathematical foundations like linear algebra, calculus, probability, and statistics, which are critical for understanding algorithm behavior. Theoretical lectures are complemented by extensive lab work and coding assignments, ensuring you learn how to apply concepts in real-world contexts.

Advanced Tools and Programming Skills

Expect to gain hands-on experience with industry-standard tools and languages. Python is the most widely used programming language, supported by libraries like TensorFlow, Keras, PyTorch, and Scikit-learn. You’ll also work with data platforms, cloud services (AWS, Google Cloud), and development environments used in AI/ML production settings. Many courses involve building and training machine learning models, analyzing large datasets, and solving practical problems using algorithms you’ve coded from scratch.

Specializations and Electives

Many U.S. programs offer the flexibility to specialize in areas such as natural language processing (NLP), computer vision, robotics, reinforcement learning, or AI ethics. Depending on your interests and career goals, you can dive deeper into these subfields through elective modules or focused research projects.

Capstone Projects and Internships

Most AI and ML programs in the U.S. culminate in a capstone project, where students work individually or in teams to solve a real-world problem using the skills they’ve acquired. Many universities also have strong links to industry, offering internships with top tech firms, startups, and research labs. These experiences not only build your portfolio but also connect you with potential employers.

Career Support and Global Recognition

U.S. universities provide robust career services, including job placement support, resume workshops, interview prep, and alumni networking. A degree or certification from a respected American institution carries significant weight globally and opens doors to top employers in technology, finance, healthcare, and academia.

Who Should Take an AI Course in the USA?

AI programs are tailored for:

Students & Graduates of engineering, computer science, statistics, and math.

IT Professionals looking to pivot into AI or ML roles.

Business Analysts & Managers aiming to incorporate AI into strategic decision-making.

Entrepreneurs & Innovators seeking to build AI-powered products.

Career Switchers with analytical thinking and a desire to learn technical skills.

Some beginner-friendly courses include foundational modules that help you transition into AI—even without a tech background.

Best Learning Formats: Online, On-Campus, or Hybrid?

The USA offers flexibility in how you can learn AI:

On-Campus Courses: Perfect for full-time students or international learners wanting immersive education and networking.

Online Courses: Great for working professionals or those needing flexibility. Many top-tier programs offer live classes, project support, and global certification.

Hybrid Programs: Combine the best of both worlds—classroom learning and online flexibility.

Top Career Paths After Completing an AI Course in the USA

With AI integration across all sectors, the job market is thriving. Graduates can pursue roles such as:

AI Engineer

Machine Learning Engineer

Data Scientist

NLP Engineer

Computer Vision Specialist

AI Research Associate

Business Intelligence Analyst

AI Product Manager

The average salary for AI professionals in the U.S. ranges from $100,000 to $160,000+ depending on experience and specialization.

How to Choose the Right AI Course in the USA?

Choosing the right AI course in the USA can significantly impact your learning experience and career trajectory. With so many options available, it’s important to consider a variety of factors to ensure the program aligns with your goals, background, and aspirations. Here’s a guide on how to choose the right AI course in the USA:

1. Assess Your Skill Level and Background

Before selecting a course, evaluate your current knowledge and skill level. AI and machine learning courses often require a solid understanding of mathematics, programming, and data science. If you are a complete beginner, consider starting with introductory courses in Python, linear algebra, and basic statistics. If you already have experience in computer science or data science, you can choose more advanced programs that dive deeper into specific AI areas such as deep learning, computer vision, or natural language processing (NLP).

2. Consider Your Career Goals

AI encompasses a broad range of specializations, so it's important to align your course selection with your career aspirations. For example:

If you're interested in data science or business intelligence, look for courses that focus on machine learning algorithms, data analysis, and big data technologies.

If you're drawn to robotics or autonomous systems, seek programs that integrate robotics engineering, reinforcement learning, and sensor systems.

For those focused on AI ethics or policy-making, programs offering courses in AI governance, fairness, and privacy are essential.

3. Program Format and Flexibility

AI courses in the USA are offered in various formats:

Full-time degree programs (Master’s or Ph.D.) offer in-depth learning, access to academic research, and the possibility of becoming an AI researcher or specialist.

Part-time programs and bootcamps are ideal if you want to study while working or if you prefer a more condensed, skills-based approach.

Online courses provide flexibility and are a great choice for self-motivated learners. These programs are often more affordable and allow you to balance studies with work or other commitments.

4. Reputation of the Institution

The reputation of the institution offering the AI course is critical in determining the quality of the program. Renowned universities like MIT, Stanford University, Harvard University, and Carnegie Mellon University are famous for their AI research and robust AI programs. These institutions not only provide top-tier education but also have extensive industry connections, increasing your chances of landing internships or jobs with leading tech companies.

However, there are also reputable online platforms and bootcamps (like Udacity, Coursera, or DataCamp) that partner with top universities and offer quality AI education, often at a lower cost.

5. Curriculum and Specialization Areas

Ensure that the course you choose offers a curriculum that aligns with the areas of AI you wish to explore. Some programs may focus broadly on AI, while others offer more niche topics. Look for courses that include:

Core AI concepts: Machine learning, neural networks, reinforcement learning, etc.

Specializations: Natural language processing, computer vision, robotics, or deep learning.

Real-world projects: Hands-on experience working with datasets, building models, and solving industry problems.

6. Industry Connections and Networking Opportunities

Look for programs that provide opportunities to connect with professionals in the AI field. Networking opportunities such as guest lectures, hackathons, industry projects, and alumni networks can be invaluable. Many AI programs in the USA have partnerships with leading tech companies, offering students internships and direct exposure to real-world AI applications.

7. Cost and Financial Aid Options

AI courses, especially those at prestigious institutions, can be expensive. Ensure that you understand the tuition fees, and look for programs that offer financial aid, scholarships, or payment plans. Some online platforms also offer free courses or affordable certification programs, which can be a great way to explore AI at a lower cost before committing to a full-fledged degree program.

Final Thoughts

Choosing an Artificial Intelligence course in the USA is more than just enrolling in a program—it's stepping into the future of work. With cutting-edge curriculum, global networking, and a robust job market, the U.S. offers the ideal environment to master AI skills and launch a high-growth career.

Whether you're looking to become a machine learning expert, develop innovative AI products, or transition into a data-driven role, the right course in the USA can set you on the path to success.

#Artificial Intelligence course in the USA #AI courses in the USA #Artificial Intelligence programs in the USA #Best AI courses USA

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pallaviicert · 2 months ago

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Artificial Intelligence Tutorial for Beginners

In the speedy digital age of today, Artificial Intelligence (AI) has progressed from science fiction to real-world reality. From virtual assistants like Siri and Alexa to intelligent suggestion algorithms on Netflix and Amazon, AI pervades all. For starters interested in this exciting discipline, this tutorial is an inclusive yet easy guide to introduce you to it. What is Artificial Intelligence? Artificial Intelligence is the field of computer science that deals with creating machines and programs which can complete tasks typically requiring human intelligence. Such tasks are problem-solving, learning, planning, speech recognition, and even creativity. In other words, AI makes it possible for computers to simulate human behavior and decision-making. Types of Artificial Intelligence AI can be classified into three categories broadly: 1. Narrow AI (Weak AI): AI systems created for a single task. Example: Spam filters, facial recognition software. 2. General AI (Strong AI): Theoretical notion where AI possesses generalized human mental capacities. It is capable of resolving new problems on its own without human assistance. 3. Super AI: Super-intelligent machines that will one day exceed human intelligence. Imagine the super-sophisticated robots of films! Most of the AI that you currently encounter is narrow AI.

Key Concepts Novices Need to Familiarize Themselves With Before going any deeper, there are some key concepts one needs to be familiar with: • Machine Learning (ML): A discipline of AI wherein machines learn from experience and are enhanced over a period of time without being specially programmed. • Deep Learning: A form of specialized ML that is inspired by the anatomy of the human brain and neural networks. • Natural Language Processing (NLP): A subdivision dealing with computers and human (natural) language interaction. NLP is used by translation software and chatbots.

• Computer Vision: Training computers to learn and make decisions with visual information (videos, images). • Robotics: Interfusion of AI and mechanical engineering to create robots that can perform sophisticated operations. How Does AI Work? In essence, AI systems work in a very straightforward loop: 1. Data Collection: AI requires huge volumes of data to learn from—images, words, sounds, etc. 2. Data Preprocessing: The data needs to be cleaned and prepared before it is input into an AI model. 3. Model Building: Algorithms are employed to design models that can recognize patterns and make choices.

4. Training: Models are trained by tweaking internal parameters in order to achieve optimized accuracy. 5. Evaluation and Tuning: The performance of the model is evaluated, and parameters are tweaked to improve its output. 6. Deployment: After the model performs well, it can be incorporated into applications such as apps, websites, or software.

Top AI Algorithms You Should Learn Although there are numerous algorithms in AI, following are some beginner-level ones: • Linear Regression: Performs a numerical prediction based on input data (e.g., house price prediction). • Decision Trees: Decision tree model based upon conditions.

• K-Nearest Neighbors (KNN): Classifies the data based on how close they are to labeled instances. • Naïve Bayes: Probabilistic classifier. • Neural Networks: As derived in the human brain pattern, used in finding complex patterns (like face detection). All these algorithms do their respective tasks, and familiarity with their basics is necessary for any AI newbie.

Applications of AI in Real Life To realize the potentiality of AI, let us see real-life applications: • Healthcare: AI assists in diagnosis, drug development, and treatment tailored to each individual. • Finance: AI is extensively employed in fraud detection, robo-advisors, and algorithmic trading. • Entertainment: Netflix recommendations, game opponents, and content creation. • Transportation: Self-driving cars like autonomous cars use AI to navigate. • Customer Service: Chatbots and automated support systems offer around-the-clock service. These examples show AI isn't just restricted to tech giants; it's impacting every sector.

How to Begin Learning AI? 1. Establish a Strong Math Foundation: AI is extremely mathematics-dependent. Focus specifically on: •Linear Algebra (matrices, vectors) •Probability and Statistics •Calculus (foundational for optimization) 2. Acquire Programming Skills: Python is the most in-demand language for AI because of its ease and wide range of libraries such as TensorFlow, Keras, Scikit-learn, and PyTorch.

3. Understand Data Structures and Algorithms: Master the fundamentals of programming in order to code effectively. 4. Finish Beginner-friendly Courses: Certain websites one must visit are: •Coursera (Andrew Ng's ML Course) •tedX •Udacity's Nanodegree courses 5. Practice on Projects Practice by creating small projects like: • Sentiment Analysis of Tweets • Image Classifiers • Chatbots • Sales Prediction Models

6. Work with the Community: Participate in communities such as Kaggle, Stack Overflow, or AI sub-reddits and learn and keep up with others.

Common Misconceptions About AI 1. AI is reserved for geniuses. False. Anyone who makes a concerted effort to learn can master AI. 2. AI will replace all jobs. Although AI will replace some jobs, it will generate new ones as well. 3. AI has the ability to think like a human. Current AI exists as task-specific and does not actually "think." It processes data and spits out results based on patterns. 4. AI is flawless. AI models can err, particularly if they are trained on biased or limited data.

Future of AI The future of AI is enormous and bright. Upcoming trends like Explainable AI (XAI), AI Ethics, Generative AI, and Autonomous Systems are already charting what the future holds.

• Explainable AI: Designing models which are explainable and comprehensible by users. • AI Ethics: Making AI systems equitable, responsible, and unbiased. • Generative AI: Examples such as ChatGPT, DALL•E, and others that can generate human-like content. • Edge AI: Executing AI algorithms locally on devices (e.g., smartphones) without cloud connections.

Final Thoughts Artificial Intelligence is no longer a distant dream—it is today's revolution. For starters, it may seem overwhelming at first, but through consistent learning and practicing, mastering AI is very much within reach. Prioritize establishing a strong foundation, work on practical projects, and above all, be curious. Remember, each AI mastermind was once a beginner like you! So, grab that Python tutorial, get into some simple mathematics, enroll in a course, and begin your journey into the phenomenal realm of Artificial Intelligence today. The world is waiting!

Website: https://www.icertglobal.com/course/artificial-intelligence-and-deep-learning-certification-training/Classroom/82/3395

#ai #training #course

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bobbyyoungsworld · 3 months ago

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The Power of Knowledge-Based Agents in AI: Transforming Decision-Making

Artificial Intelligence (AI) is no longer just about automation—it’s about intelligence that can think, learn, and adapt. One of the most sophisticated advancements in AI is the Knowledge-Based Agent (KBA), a specialized system designed to make informed, rule-based decisions by leveraging structured data, inference engines, and logical reasoning.

With industries increasingly relying on AI-driven solutions, Knowledge-Based Agents are becoming essential in streamlining processes, enhancing accuracy, and making real-time decisions that drive business growth.

What is a Knowledge-Based Agent in AI?

A Knowledge-Based Agent is an intelligent AI system that stores, retrieves, and applies knowledge to make well-reasoned decisions. Unlike traditional reactive AI models, KBAs use a structured knowledge base to:

✔ Analyze input data using logic-based reasoning

✔ Apply stored rules and facts to infer conclusions

✔ Adapt to new information and learn from outcomes

These agents are widely used in fields like healthcare, finance, automation, and robotics, where precision and reliability are crucial.

How Knowledge-Based Agents Differ from Other AI Models

Traditional AI models often rely on pattern recognition and probabilistic learning. In contrast, KBAs focus on logical reasoning by utilizing explicit knowledge representation and inference mechanisms. This makes them highly effective in areas requiring:

Complex decision-making with multiple rules and conditions

Transparent and explainable AI models for compliance-driven industries

Scalable automation that integrates seamlessly with other AI systems

8 Key Features of Knowledge-Based Agents in AI

1. Knowledge Representation 🧠

A KBA structures raw data into meaningful insights by encoding facts, rules, and relationships. This knowledge is stored in various formats such as:

🔹 Semantic Networks – Links concepts for easy visualization

🔹 Ontological Models – Defines relationships using a structured vocabulary

🔹 Rule-Based Engines – Uses if-then logic to execute predefined decisions

By organizing knowledge efficiently, KBAs ensure clarity, adaptability, and interoperability, making AI-driven decision-making more reliable.

2. Inference & Reasoning Capabilities 🏗️

KBAs use advanced logical reasoning techniques to process data and derive conclusions. Key reasoning methods include:

✔ Deductive Reasoning – Deriving specific conclusions from general rules

✔ Inductive Reasoning – Identifying patterns based on observed data

✔ Abductive Reasoning – Finding the most likely explanation for incomplete information

These methods enable KBAs to simulate human-like decision-making with high accuracy, even in uncertain environments.

3. Learning & Adaptation 📈

Unlike static rule-based systems, modern KBAs integrate machine learning to improve over time. By incorporating:

🔹 Supervised Learning – Training with labeled data

🔹 Unsupervised Learning – Identifying patterns without predefined categories

🔹 Reinforcement Learning – Learning through feedback and rewards

KBAs evolve dynamically, making them invaluable for industries requiring continuous improvement, such as predictive analytics and fraud detection.

4. Problem-Solving & Decision-Making 🤖

A KBA follows structured frameworks to analyze problems, evaluate options, and make optimal decisions. It does this by:

✔ Processing real-time data to generate actionable insights

✔ Applying constraint-based reasoning to narrow down possible solutions

✔ Using predictive analytics to forecast potential outcomes

This feature makes KBAs essential in industries like finance, supply chain management, and healthcare, where accurate decision-making is vital.

5. Interaction with the Environment 🌎

KBAs interact with their surroundings by integrating sensor inputs and actuator responses. This enables real-time adaptability in applications like:

🔹 Autonomous vehicles – Processing road conditions and responding instantly

🔹 Industrial automation – Adjusting workflows based on sensor feedback

🔹 Smart healthcare systems – Monitoring patient data for proactive care

These agents capture environmental data, analyze it efficiently, and take appropriate actions in milliseconds.

6. Multi-Agent Collaboration 🤝

In distributed AI systems, multiple KBAs can collaborate to optimize decision-making. This is crucial in fields like:

✔ Smart Traffic Systems – Coordinating signals to ease congestion

✔ Robotics & Manufacturing – Managing tasks across multiple AI agent development company

✔ Supply Chain Optimization – Enhancing logistics through shared data processing

By working together, KBAs maximize efficiency and scalability in complex operational environments.

7. Explainability & Transparency 🔍

One of the biggest challenges in AI is explainability. KBAs provide clear decision paths using:

🔹 Decision Trees – Visualizing choices in a step-by-step format

🔹 Rule-Based Systems – Offering simple, traceable logic

🔹 Attention Mechanisms – Highlighting key factors influencing decisions

This ensures compliance with AI regulations and enhances trust and accountability in industries like finance, law, and healthcare.

8. Integration with Other AI Technologies ⚙️

KBAs don’t work in isolation—they seamlessly integrate with Machine Learning (ML), Natural Language Processing (NLP), and Blockchain to enhance functionality.

✔ ML Integration – Recognizes patterns and predicts outcomes

✔ NLP Capabilities – Understands human language for better interaction

✔ Blockchain Connectivity – Secures data and ensures transparency

This enables KBAs to power intelligent chatbots, automated compliance systems, and AI-driven financial models.

Why Businesses Should Adopt Knowledge-Based Agents

From automating operations to enhancing strategic decision-making, KBAs offer multiple advantages:

✔ Faster, More Accurate Decisions – Reduces manual intervention and errors

✔ Scalability & Efficiency – Handles complex problems seamlessly

✔ Regulatory Compliance – Ensures transparent and explainable AI-driven processes

✔ Competitive Advantage – Helps businesses stay ahead in the AI-driven economy

Industries such as finance, healthcare, cybersecurity, and e-commerce are already leveraging KBAs to streamline workflows and boost profitability.

The Future of Knowledge-Based Agents in AI

As AI continues to evolve, Knowledge-Based Agents will play a pivotal role in shaping the next generation of intelligent automation. The integration of deep learning, blockchain, and NLP will further enhance their capabilities, making them indispensable for modern enterprises.

🚀 Are you ready to implement AI-driven decision-making? At Shamla Tech, we specialize in developing custom AI solutions powered by Knowledge-Based Agents. Our expertise helps businesses achieve unmatched efficiency, accuracy, and scalability.

📩 Let’s build the future of AI together! Contact us today for a free consultation.

#ai agent development company #ai agent development #agent in artificial intelligence #ai development #ai agent developer #knowledge based agent in ai #ai agent #types of agent in ai #ai developers

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jcmarchi · 4 months ago

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AI in Marketing: The Ultimate Growth Co-Pilot

New Post has been published on https://thedigitalinsider.com/ai-in-marketing-the-ultimate-growth-co-pilot/

AI in Marketing: The Ultimate Growth Co-Pilot

It’s 2025 and Machine Learning is in full swing and the hot topic at every marketing conference across the globe. The rise of AI-powered martech (marketing technology) promises to make advertising better, accelerate creative development while deciphering large amounts of data, and make human-like decisions. Further proof: a recent report states that 80% of CMOs plan to increase spending on AI and data in 2025.

Over the last decade, marketing leaders have been inundated with data, unrealistic pressure to “make things viral,” measure everything, along with an evasive and evolving consumer.

It’s the perfect recipe for a technology that can analyse data at scale and optimise a near infinite amount of outcomes in milliseconds. Have we found the holy grail?

AI in its current form is very helpful but not magical…yet. From video production and copywriting to SEO and ad creation, AI can create a powerful co-pilot that can fast-track creative thinking, speed up processes, and enhance human ingenuity, accelerating strategic thinking rather than replacing it.

Let’s look briefly at how marketing got here:

From digital boom to data overload

The 2006 digital marketing boom promised a totally trackable experience for advertisers. From ROI to detailed cookie targeting, and optimised budget distribution. Yes, many of these promises came true, to some extent. Clicks, conversions, and cost-per-acquisition are now more accurate for digital marketers. E-commerce giants like eBay who spent millions on Google Search Ads and Facebook marketing overwhelmingly succeeded.

The pressure to be digital only with clear ROI was immense and the Creative Marketer was pushed out by the data gurus. However, this caused issues. Gartner Research in 2022 states that 60% of CMOs struggled to turn data into actionable insights. The quest for total attribution or clarity led to probabilistic modeling and confusion. ROI Multipliers dropped in some cases from 6:1 to 2:1 on some platforms. GDPR, Data privacy, cookie policies, VPN’s, and the rise of walled gardens like Facebook and Apple have drastically limited marketers from securing first-party data.

Here comes the robots!

The marketing landscape is constantly changing, but the lack of clarity and massive amounts of data have made it ripe for an AI revolution.

Machine learning can pull data, find trends and commonalities in large amounts of information. It can look at the data, learn from it, and discard without storage. It can understand client behaviours and preferred actions. Both Meta Ads Manager and Google Ads have shifted to AI-powered bidding tools, and their creative suite creates incredible ad imagery and copy in seconds.

The benefits from AI are easy to measure. Time savings, easy-to-understand insights, and the ability to scale.

Savings with Automation – AI-driven platforms can automate email marketing, creative design, and data mining. According to Deloitte, using AI automation reduced operating expenses for 71% of marketing professionals in 2023.

Decisions Based on Data – Machine Learning can start to look at making decisions with the data at hand—guiding strategic moves, predicting outcomes, and measuring results. Budget allocation will always be a challenge that marketers have, and AI can help. There will be a shift from instinctive to data-based planning, leading to better outcomes for marketers.

Less with More – Smaller teams will now be able to do more. With automating procurement deals, creative cold starts, and performance, they should be able to free up time to focus on what’s important: their customers. Mid-market companies who weren’t able to afford data scientists like P & G will now have the same capability at a fraction of the cost.

There is a lot of excitement around AI, but with anything there is a potential downside. Over-automation can lead to content similarities. Marketing has always been about zigging when others are zagging. AI creates content that has a high probability of being liked, therefore likely similar. Email subject lines and advertising that follow a similar ML script will likely create a boring experience for consumers. Using AI to create brand narratives and positioning is at a high risk for being copied and left behind.

With an over-reliance on performance, brands can easily lose their identities. Retaining a brand voice in an era of similarity will be key to success. Conversions may not reward human creativity, putting it on the chopping block for performance. Ultimately eroding brand identity and its unique selling points.

Additionally, with the rise of AI impersonating human creativity, scepticism is at an all-time high. Reddit forums are full of complaints about advertisers using AI and sharp-eyed sleuths calling them out. If consumers feel that AI is having their way with their personal data, they can feel violated and uneasy. A 2019 Pew Center research study found that 81% of Americans have no control over their data. AI will likely increase distrust and put consumers on the back foot, leading to the rise of Ethical AI. The good news is that this leads to studies showing that 70% of enterprise CMOs will prioritize ethical AI (privacy and secure data) in marketing by 2025.

Ethical AI

How do we integrate artificial intelligence without losing emotion? By keeping a human in the loop. AI is certainly the future and it will be at the forefront of science, technology, and many other industries. In marketing, keeping a human in the loop will add cultural significance, intuitive awareness, brand context, and certain areas of creativity that machines won’t understand. Creativity comes from thinking outside of the box and shocking a normal brain pattern to create a memorable experience. This will be difficult for AI. A real human marketer can curate, adjust, and find ideas that match brand values and audience behaviours in lockstep with their machine learning co-pilots.

Ethical AI is a new term for marketing. Reviewing each campaign and understanding the data it’s using are guardrails that will protect brands and help them navigate through a changing world.

AI will be great at predicting customer behaviours, but true creativity will turn touchpoints into memorable moments. Pioneering brands that use data for targeting and storytelling will stand out, where the machines will tell them who and when—it’s the creatives that will provide the why.

Where does AI in marketing go? Agentic, driverless, autonomous!

Agent hubs are on the rise, with platforms like SalesForces, Agent Force, HubSpots, Agent.ai, and Xenet.ai’s hub marketing agents that will plug in and do their jobs. The “agentic” or autonomous marketing world will open the door to data science agents, PR agents, creative agents, AB testers, and more—all to be run without human intervention. The top line strategy, creative execution, and overall direction will require humans in the loop.

The rise of AI will benefit Mid-market businesses the most. Large companies can and have hired data scientists for years, whereas small businesses were stuck with “set and forget” techniques in Google, Meta, and other platforms. An Agentic marketing tool, would deliver enterprise-level abilities at a lower cost. Think of it like an AI “data scientist agent” that understands, reviews, predicts, and decides every second.

The good news for martech: there is no winner-take-all. Results as a service will be on the rise. Disruptors will always be in the bush waiting to take out largely funded players. Money is no longer the advantage—it’s intelligence, timing, and strategy. Agents that show real ROI will be trusted partners in a $1trillion dollar industry.

Balancing AI and Human Ingenuity

In conclusion, CMO’s will benefit greatly from the advancements in AI. Automation will cut costs, time, and effort.

AI is not the panacea, but a symphony of tech and humans will lead the way. Good marketing demands creativity, intelligence, and strategy. An over-reliance on automation can water down a brand’s messaging, wear away unique positioning, and drive away customers. AI is a great co-pilot, and it will drive precision and efficiency at scale, while humans in the loop will lead creativity and ethics. As agentic, driverless marketing grows, humans and algorithms must be balanced carefully. In a world of unlimited choice and limited budgets, marketing teams who use AI’s power without sacrificing the emotional connection to their brand will succeed.

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educationtech · 4 months ago

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9 Important Tips for First-Year Engineering Students - Arya College

For B.Tech first-year students, Arya College of Engineering & I.T. which is the best Engineering college in Jaipur says success involves a combination of academic focus, skill development, and social integration. Here's a comprehensive guide to help you navigate your first year:

1. Academics and Skill Development:

Maintain good grades: Good grades are beneficial. Focus on understanding core subjects such as mathematics, physics, computer programming, engineering graphics, and basic engineering principles.

Explore specializations: B.Tech degrees offer various specializations like Computer Science, Electronics and Communication, Mechanical, Civil, Electrical, Chemical, Aerospace, and more.

Technical, analytical, and problem-solving skills: B.Tech programs are designed to impart a range of technical, analytical, and problem-solving skills, along with a solid grounding in mathematics and science.

Certification courses: Pursue certification courses to become more employable.

Stay updated: Update yourself with new technology and topics related to core subjects. Open yourself to every opportunity for learning.

2. Extracurricular Activities and Social Life:

Find college friends: During the first few weeks of school, be social, as these people will grow with you and be very influential in your life for the next four years.

Have fun, but don't make choices you will regret: Your first year of college life is when you’re supposed to go crazy, but don’t make choices you’ll regret.

Develop your social skills: Focus on developing your social skills.

3. Projects and Internships:

Projects and internships: Do various projects and internships to showcase on your resume.

4. Mindset and Goals:

Embrace opportunities: Open yourself to every opportunity for learning.

Avoid lagging behind: Work to avoid lagging behind peers in better colleges by developing an amazing skillset.

By focusing on these key areas, B.Tech first-year students can lay a strong foundation for a successful and fulfilling college experience.

What are the best certification courses for a first-year BTech student

For first-year B.Tech students, certification courses can enhance skills and broaden career prospects. Here are some of the best options to consider:

Job-Oriented Courses:

These short-term training programs equip individuals with specific skills and knowledge required for particular job roles or industries. They focus on practical and hands-on learning, making participants job-ready and enhancing their employability.

Specific Certification Courses:

Cybersecurity Certification: Because of the increase in data threats and hackers, there is a high demand for skilled individuals in the field of Cybersecurity. The cybersecurity certification program helps you learn about the nature of cyberattacks, how to recognize online risks, and how to take preventative actions.

Data Science Certification: The increased demand for Big Data skills & technologies introduced certification programs in data science. The course provides learning about the data management technologies such as Hadoop, R, Flume, Sqoop, Machine Learning, Mahout, etc.

Data Analyst: The data analyst certification helps learners develop skills like critical thinking & problem-solving.

Web Designing: B.Tech CSE students with an additional certification in web development have higher chances to be selected as software engineers, UX/UI designers, web designers & web developers.

Full Stack Web Development:

Python Programming:

Artificial Intelligence:

Matrix Algebra for Engineers: This certification emphasizes linear algebra that an engineer should know.

Artificial Intelligence for Robotics: This course covers basic methods in AI such as probabilistic inference, planning, search, localization, tracking, and control, all focusing on robotics.

Technical Report Writing for Engineers: This course introduces the art of technical writing and teaches engineers the techniques to construct impressive engineering reports.

Embedded Systems - Shape The World: Microcontroller Input/ Output: This course will teach you to solve real-world problems using embedded systems

#Engineering

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aryacollegeofengineering · 4 months ago

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A Complete Success Guide for BTech First-Year Students

1. Academics and Skill Development:

Maintain good grades: Good grades are beneficial. Focus on understanding core subjects such as mathematics, physics, computer programming, engineering graphics, and basic engineering principles.

Explore specializations: B.Tech degrees offer various specializations like Computer Science, Electronics and Communication, Mechanical, Civil, Electrical, Chemical, Aerospace, and more.

Certification courses: Pursue certification courses to become more employable.

Stay updated: Update yourself with new technology and topics related to core subjects. Open yourself to every opportunity for learning.

2. Extracurricular Activities and Social Life:

Find college friends: During the first few weeks of school, be social, as these people will grow with you and be very influential in your life for the next four years.

Have fun, but don't make choices you will regret: Your first year of college life is when you’re supposed to go crazy, but don’t make choices you’ll regret.

Develop your social skills: Focus on developing your social skills.

3. Projects and Internships:

Projects and internships: Do various projects and internships to showcase on your resume.

4. Mindset and Goals:

Embrace opportunities: Open yourself to every opportunity for learning.

Avoid lagging behind: Work to avoid lagging behind peers in better colleges by developing an amazing skillset.

By focusing on these key areas, B.Tech first-year students can lay a strong foundation for a successful and fulfilling college experience.

What are the best certification courses for a first-year BTech student

For first-year B.Tech students, certification courses can enhance skills and broaden career prospects. Here are some of the best options to consider:

Job-Oriented Courses:

Specific Certification Courses:

Data Analyst: The data analyst certification helps learners develop skills like critical thinking & problem-solving.

Web Designing: B.Tech CSE students with an additional certification in web development have higher chances to be selected as software engineers, UX/UI designers, web designers & web developers.

Full Stack Web Development:

Python Programming:

Artificial Intelligence:

Matrix Algebra for Engineers: This certification emphasizes linear algebra that an engineer should know.

Artificial Intelligence for Robotics: This course covers basic methods in AI such as probabilistic inference, planning, search, localization, tracking, and control, all focusing on robotics.

Technical Report Writing for Engineers: This course introduces the art of technical writing and teaches engineers the techniques to construct impressive engineering reports.

Embedded Systems - Shape The World: Microcontroller Input/ Output: This course will teach you to solve real-world problems using embedded systems.

Source: Click here

#best btech college in jaipur #best engineering college in jaipur #best btech college in rajasthan #best private engineering college in jaipur

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nishtha135 · 5 months ago

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What’s the ideal mix of skills for a successful AI developer?

The ideal mix of skills for a successful AI developer combines a blend of technical knowledge, problem-solving abilities, and a deep understanding of AI principles. Here’s a breakdown.

1. Programming Skills:

Python: The most widely used language for AI/ML due to its libraries (e.g., TensorFlow, Keras, PyTorch).

C++: Useful for performance-intensive AI applications.

R: Popular for statistical computing and data analysis.

2. Mathematics and Statistics:

Linear Algebra: For understanding neural networks, transformations, and data representation.

Calculus: Essential for optimization problems and training models.

Probability and Statistics: Critical for building probabilistic models, analyzing data, and making predictions.

3. Machine Learning & Deep Learning:

Algorithms: Understanding algorithms like regression, classification, clustering, and decision trees.

Neural Networks: Knowledge of deep learning architectures (e.g., CNNs, RNNs, GANs) for tasks like image recognition and NLP.

Model Training and Evaluation: Experience in training models and evaluating their performance (using metrics like accuracy, precision, recall, etc.).

4. Data Handling:

Data Preprocessing: Cleaning and preparing datasets for training models.

Data Visualization: Using tools like Matplotlib, Seaborn, or Tableau to visualize data insights.

Big Data Tools: Familiarity with tools like Hadoop or Spark can be helpful for processing large datasets.

5. Software Engineering Practices:

Version Control: Proficiency with Git for collaboration and code management.

APIs and Frameworks: Familiarity with building and integrating AI models through APIs (RESTful services) or cloud platforms like AWS and Google Cloud.

6. Domain Knowledge:

Understanding the specific domain in which you are applying AI (e.g., healthcare, finance, robotics) can be critical for creating effective AI solutions.

7. Soft Skills:

Problem-Solving: Ability to break down complex problems and find AI-driven solutions.

Continuous Learning: AI is a rapidly evolving field, so staying up-to-date with new research, algorithms, and tools is essential.

By combining these skills, an AI developer can effectively tackle real-world problems and innovate in the field of artificial intelligence.

#ai #artificial intelligence

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learning-robotics · 11 months ago

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Best Robotics Papers in 202

What Are the Best Robotics Papers?

The field of robotics is rapidly evolving, with groundbreaking research and innovative developments happening at an unprecedented pace. For those deeply entrenched in this field or simply curious about the latest advancements, understanding the most influential and highly-regarded robotics papers is crucial. This article delves into some of the best robotics papers that have significantly contributed to the field, highlighting their key findings, methodologies, and impacts.

Introduction to Robotics Research

Robotics research encompasses a wide array of topics, from artificial intelligence and machine learning to mechanical design and human-robot interaction. Each of these areas contributes to the overall advancement of robotics, making it a multidisciplinary field that requires a comprehensive understanding of various scientific principles and technologies.

Key Areas of Robotics Research

Artificial Intelligence and Machine Learning

AI and machine learning are at the heart of modern robotics, enabling robots to perform complex tasks, learn from their environment, and adapt to new situations. Some of the most influential papers in this area include:

"Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm" by Silver et al.

Summary: This paper introduces AlphaZero, an AI system that uses reinforcement learning to master chess and shogi without prior knowledge of the games.

Impact: Demonstrates the power of reinforcement learning in developing AI that can learn and outperform humans in complex tasks.

"DQN: Playing Atari with Deep Reinforcement Learning" by Mnih et al.

Summary: The paper presents a deep Q-network (DQN) that combines reinforcement learning with deep neural networks to play Atari games at a superhuman level.

Impact: Showcases the potential of deep learning in developing AI agents capable of complex decision-making processes.

Mechanical Design and Control

Mechanical design and control are fundamental to the development of efficient and functional robots. Notable papers in this domain include:

"Passive Dynamic Walking" by McGeer

Summary: This pioneering work introduces the concept of passive dynamic walking, where robots use gravity and inertia to achieve efficient, human-like gait patterns without active control.

Impact: Revolutionizes the approach to robotic locomotion, emphasizing energy efficiency and simplicity.

"BigDog, the Rough-Terrain Quadruped Robot" by Raibert et al.

Summary: Describes the development of BigDog, a quadruped robot capable of navigating rough terrain using advanced control algorithms and mechanical design.

Impact: Advances the field of legged robotics, showcasing the potential for robots to operate in challenging environments.

Human-Robot Interaction

Human-robot interaction (HRI) is a critical area of research, focusing on how robots and humans can work together effectively. Key papers in this field include:

Breakthrough Robotics Papers

"Planning Algorithms" by LaValle

Summary: This comprehensive book covers a wide range of planning algorithms essential for robotics, including motion planning, discrete planning, and planning under uncertainty.

Impact: Serves as a foundational reference for researchers and practitioners in the field of robotics planning.

"Probabilistic Robotics" by Thrun, Burgard, and Fox

Summary: Introduces probabilistic methods for robot perception, localization, and mapping, emphasizing the importance of uncertainty in robotic systems.

Impact: Establishes a new paradigm in robotics, where probabilistic approaches are integral to developing robust and reliable robots.

"The DARPA Robotics Challenge Finals: Humanoid Robots To The Rescue" by Pratt et al.

Summary: Details the DARPA Robotics Challenge, a competition aimed at developing humanoid robots capable of performing complex tasks in disaster response scenarios.

Impact: Highlights the advancements and challenges in creating humanoid robots that can operate in real-world disaster situations.

Emerging Trends in Robotics Research

Swarm Robotics

Swarm robotics involves the coordination of multiple robots to achieve collective behavior. Key papers include:

"Swarm Intelligence: From Natural to Artificial Systems" by Bonabeau, Dorigo, and Theraulaz

Summary: Explores the principles of swarm intelligence and their application to robotics, drawing inspiration from natural systems like ant colonies and bird flocks.

Impact: Provides a comprehensive framework for understanding and developing swarm robotics systems.

"Kilobot: A Low-Cost Scalable Robot System for Demonstrating Collective Behaviors" by Rubenstein et al.

Summary: Introduces Kilobot, a low-cost, scalable robotic system designed to study collective behaviors in large robot swarms.

Impact: Demonstrates the feasibility of large-scale swarm robotics and its potential applications.

Soft Robotics

Soft robotics focuses on creating robots with flexible, deformable bodies that can adapt to their environment. Influential papers include:

"Soft Robotics: A Bioinspired Evolution in Robotics" by Laschi and Cianchetti

Summary: Discusses the principles and applications of soft robotics, inspired by biological systems like octopuses and worms.

Impact: Highlights the potential of soft robots in areas where traditional rigid robots are limited.

"Soft Robots for Chemists" by Whitesides

Summary: Explores the interdisciplinary nature of soft robotics, particularly its applications in chemistry and biomedical engineering.

Impact: Bridges the gap between robotics and other scientific disciplines, fostering innovation and collaboration.

Conclusion

The field of robotics is a dynamic and rapidly evolving area of research, driven by groundbreaking papers that push the boundaries of what is possible. From AI and machine learning to mechanical design, human-robot interaction, and emerging trends like swarm and soft robotics, these papers have laid the foundation for the future of robotics. By understanding and building upon these seminal works, researchers and practitioners can continue to advance the field, creating robots that are more intelligent, capable, and adaptable than ever before.

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#robotics #100 days of productivity #academia #artificialintelligence #artificial intelligence #audi #accounting #bujo spread #bmw #autos #Pragrobotics

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arcitechaiblog · 2 years ago

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What is the Future of AI? #Expertpredictions

https://arcitech.ai/wp-content/uploads/2023/12/future-of-artificial-intelligence.jpg

Introduction

Artificial Intelligence has evolved into a mentor, companion, and more for individuals globally. With the capacity to answer nearly any query and the skill to ‘think’, it’s remarkably aced various tests designed to assess human cognition and reasoning. The AI revolution has arrived! But the question remains: will it endure, or will a new human invention surpass it? Discover the anticipated future of AI across various domains in this article.

“ AI is one of the most important things humanity is working on. It is more profound than electricity or fire. “

Future of AI: How It Was Viewed and Predicted 10 Years Ago.

The idea of AI has been capturing the imagination of people for a long time, way before we even had a name for it. It was both exciting and a bit scary to think about making machines that were like us. We often thought that smart machines had to look human, but actually, AI was already doing great things. For example, AI was better than humans at chess (Hsu, 2002), the game Go (Silver et al., 2016), and translating languages (Wu et al., 2016), and these were big news. But even before these, AI had been used in industries since the 1980s.

Back then, AI systems called “expert” or rule-based systems were used for things like checking circuit boards and spotting credit card fraud. Also, machine learning, which includes methods like genetic algorithms, was used to solve tough problems like planning schedules. And neural networks, which try to work like human brains, were used for understanding how we learn and for important jobs in industry like control and monitoring.

The 1990s brought a big change in machine learning with new methods like probabilistic and Bayesian approaches. These laid the groundwork for the AI tools we use a lot today, like going through huge amounts of data. This meant that people could search and make sense of billions of web pages just by typing a few words. (Lowe, 2001; Bullinaria and Levy, 2007).

Looking back, we can see that AI has come a long way and has achieved a lot, sometimes even before we realized what the future could hold.

Recent Technology Advancements in AI

AI is moving forward fast. Every day, there are new discoveries and uses in things like robots. Learning machines, and how computers see and understand pictures. This means less work for humans, as machines are doing more of the tasks. The biggest changes are happening in many areas, like healthcare, coding, learning, money matters, building, getting around, fun activities, law, buying and selling houses, exploring space, and shopping.

These new steps in AI are making it more automatic, changing how AI and people will work together in the future. With this, there will be more risks to keeping data safe and new questions about what’s right and wrong, which will need new rules.

Even with these challenges about ethics and data safety. AI is set to make big changes in all areas, bringing new chances and hurdles. As robots and AI handle the boring jobs. People can use their creativity and new ideas more, leading to even more discoveries and progress.

Also Read: The Top 15 AI Tools for Business in 2024 (Both Free and Paid)

What Industries Will See Changes?

AI is the next big thing in technology because of how fast it’s improving. It’s going to really change how work is done, helping everyone who gives or gets services. Here’s a list of the areas that will be most affected by AI:

1) Future of AI in Healthcare

The future of AI in healthcare is full of new ideas and big steps forward. AI helps doctors diagnose diseases faster and more correctly, make treatment plans just for you, and get better results for patients. It uses machine learning to look at huge amounts of health data, like genes, health records, and medical pictures, to find patterns and come up with new ways to treat people.

https://arcitech.ai/wp-content/uploads/2023/12/Future-of-AI-in-Healthcare-2048x1148.jpg

” Artificial intelligence is one of the most promising fields in technology and has the potential to help solve some of the world’s most pressing challenges, including healthcare. “

2) Future of AI in Education

” Education is clearly the foundation for success, and our future depends on innovation and creativity that will come from our students. “

3) Future of AI in Transportation

The big goal for using AI in transportation is to have self-driving cars and big vehicles. Right now, there aren’t any fully self-driving cars; the ones we have still need a driver to watch over them. AI is being used to make these autonomous cars, improve how we find our way, and manage traffic better. This makes getting around more efficient, safe, and easy. Recently, AI and machine learning have really moved forward in transportation, with companies like Tesla and Waymo working on self-driving car technology.

https://arcitech.ai/wp-content/uploads/2023/12/Future-of-AI-in-Transportation-1-2048x1293.jpg

” We will eventually see autonomy and AI be the only option. It will be so much safer than human drive. “

4) Future of AI in Customer Services

AI is making big changes in customer service, with new ideas coming up all the time. It gives personalized and quick help, like chatbots, digital helpers, and understanding human speech. AI chatbots can answer customer questions any time of the day, making answers faster and customers happier.

https://arcitech.ai/wp-content/uploads/2023/12/Future-of-AI-in-Customer-Services-2048x1038.jpg

” AI can help to improve customer service by automating routine tasks and providing personalized recommendations. “

5) Future of AI in Marketing

AI is on its way to really changing marketing, with new ideas and big steps forward happening all the time. It uses smart models to predict what customers will do, understands what they need and like, and uses this to make marketing better and more focused. This means reaching the right people at the right time. Making content automatically using these details will lead to marketing that feels more personal, aimed at individual people, not just groups.

https://arcitech.ai/wp-content/uploads/2023/12/Future-of-AI-in-Marketing-2048x1126.jpg

As AI continues to evolve, we can expect to see more intelligent automation in marketing, making it easier and faster to reach our audience. The possibilities are endless, and the future of marketing looks very exciting.

6) Future of AI in Human Resource Management

Cognitive computing is going to be the next big thing in human resource management. It is going to transform how we hire, train, and retain employees.

7) Future of AI in Banking

https://arcitech.ai/wp-content/uploads/2023/12/Future-of-AI-in-Banking-2048x1321.jpg

” AI and machine learning will transform every aspect of banking over the next decade. “

Common Myths About Advanced AI

Let’s look at some popular myths about advanced AI and how it’s used:

Myth 1: AI’s Accuracy Only Depends on Its Training Data

People think AI’s output is only as good as its data, which can be limited, uneven, messy, and poor quality. But really, AI’s accuracy comes from how it processes data, how problems are set up, using made-up data, choosing specific samples, and setting limits in its models. Also, AI doesn’t just rely on data. The algorithms, human skills, and the computers it runs on are just as important.

Myth 2: AI is as Smart as Humans

AI has done some amazing things, making it seem like it’s as smart as people. But it has limits. It acts based on patterns and instructions, not on its own. For example, an AI that can play a game isn’t necessarily good at making art or writing stories. Scientists are trying to give AI more skills, but making it as smart as a human is still really hard.

Myth 3: AI Will Take Away All Jobs

Some people think AI will make humans jobless. But really, it just changes the kinds of jobs we do, needing new skills. AI has replaced some jobs, but it also creates more interesting work. It makes us more productive and lets us be more creative. We just need to focus on how to use AI best, especially thinking about how it affects the economy and people’s lives.

Myth 4: AI Isn’t Really Important

AI is very useful. It helps manage money, makes predictions, and improves cash flow. It helps businesses grow by making smart plans, improving customer service, and giving warnings at the right time.

Myth 5: Businesses are Better Off Without AI

Actually, AI is really important for businesses to grow and solve problems. It uses smart models to make good predictions and understand business issues. This helps businesses grow a lot and find out what they need to fix.

Myth 6: AI Will Control Humans

It might seem like AI could control us, like in science fiction, but that’s not true. AI works by using specific goals and methods. For AI to control humans, it would need human-like thinking or consciousness. We still don’t fully understand consciousness, so we can’t expect AI to have it. AI is just a tool to help solve complex problems with methods and rules set by humans.

What’s Coming Soon in AI

The future of AI looks really good. It’s going to make things better in learning, health, getting around, and how we find information. We’ll need more people who know a lot about tech and who can solve problems. But we can’t forget the big questions about how AI affects people, like being fair, keeping private stuff safe, and what happens if AI makes a mistake.

Soon, we’ll see even better AI, like new versions of DALLE and new versions of GPT. AI will be used more in businesses for different jobs and talking to customers. The health field will grow with AI too. As AI gets used more, there will probably be new rules to make sure it’s fair and clear how it’s used.

Also Read: The Top 15 AI Tools for Business in 2024 (Both Free and Paid)

AI and Privacy Risks: Simple Points

1. Privacy Worries: We don’t know who gets private info and what they do with it.

2. Job Search Privacy: You might have to share personal details for AI job sites.

3. Who’s Responsible? When data is misused, it’s hard to know who to blame.

4. Consent Issues: Sometimes, there’s no permission asked for collecting or using data.

5. AI Watching: AI could accidentally share secret info, which might help criminals.

6. Unfair Choices: AI might be biased and share data based on its own judgments.

7. Fake Content: AI can make fake pictures or videos that are hard to trace back.

8. Hidden Data Use: Companies don’t always say how they use our data.

9. Cyberbullying Increase: More AI might lead to more online bullying and identity theft.

frequently asked questions:

Q1. What is the future of AI?

A. AI’s future looks really good. It’s getting better all the time in learning, understanding language, and seeing like humans. AI will make lots of areas better and change how we live and work.

Q2. What will AI replace in the future?

A. AI could take over jobs that are boring and done over and over, so people can do more interesting and creative things.

Q3. What is the future positive of AI?

A. AI will help do things better, faster, and with fewer mistakes in lots of areas. It’s also going to help improve health, education, and taking care of the planet.

Q4. What is the future of AI in 2050?

A. What AI will be like in 2050 isn’t clear yet, but it might be a big part of our lives. AI could help solve many big problems and bring new chances to create and grow. But how we handle big questions and rules about AI will be important too.

#futureofai #ai future #arcitech

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towardsrobotics-blog · 5 years ago

Link

Probabilistic robotics is a new and expanding place in robotics, involved with understanding and control in the face of uncertainty.

#robotics #Probabilistic Robotics #robots #what is probabilistic robotics #probabilistic

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itsuki-minamy · 3 years ago

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RED CASE FILES: HOMRA IN LAS VEGAS

CHAPTER 7: RED ENCOUNTER

* List of Chapters

Translation: Naru-kun Raws: Ridia

Fushimi Saruhiko threw himself on the couch by the window.

The night view of Las Vegas extends outside the window. The myriad of flickering neon lights of the casinos, the night shows of the luxurious first class hotels and the bright twinkling stars woven by them were truly a sight worth a million dollars.

However, the beautiful scenery did not heal Fushimi's stagnant eyes.

"Thank you for your hard work, Fushimi-san."

As he rubbed his eyes, Tanaka brought him a cup of coffee. Did he want him to work more? Looking at Tanaka's masked smile, Fushimi took the coffee and started to sip it.

"What is your progress?"

"Thanks to Fushimi-san, things are going well. We have completed the installation of W Vessensors at 17 emergency entrances and exits of the hotel. With this, if a person with supernatural powers or a supernatural weapon breaks into the hotel, we will be able to catch it quickly."

Fushimi snorted. Extraordinary weapon. It sounded ridiculous, but as someone who was actually attacked, he could never make a fool of himself. Considering that those ostrich-shaped robotic weapons could attack en masse, it couldn't be helped to make some preparations.

Fushimi looked around the room over his shoulder.

The interior of the room had been remodeled over the last few days, and it was finished in such a way that the briefing room of "Scepter 4" looked like this. More than 10 monitors are installed on the wall of the room, showing (illegally, of course) images from surveillance cameras located throughout the hotel. While stepping over wires that are so thick that there is no room to step on, the "Tokijikuin" agents wearing the same suits as Tanaka are busy going back and forth, contacting here and there, like a command room in the field of battle.

After taking a sip of coffee, Fushimi asked Tanaka next to him.

"Do you think they found us?"

"It's best to think so. It's been a while since then. Since they're based in Las Vegas, they know what we're doing."

Fushimi narrowed his eyes and began to analyze the force.

The enemy is the US Intelligence Department, the CIA, or the NSA's 100-person non-regular force, plus ridiculous robots. On the other hand, there are more than a dozen supernatural beings centered around the "Red King". Their personal impressions aside, Kusanagi and Yata have top-notch psychic abilities. Even against a fully armed soldier, it would do nothing.

"The problem is the amount of extraordinary weapons."

Tanaka immediately nodded at Fushimi's murmur.

"[Ostrich]: It's troublesome that we don't know the total number of those bipedal walking weapons. It would be safer to assume there are 10 of them, even if it's a low estimate."

"......"

Fushimi frowned.

At that moment, when the "Ostrich" attacked, almost everyone inside the van was deploying their supernatural fields.

Conventional weapons are ineffective against fields deployed by supernatural beings. The so-called probabilistic deflection field, a force acting on the probabilities of phenomena, deflects bullets from conventional weapons. No matter how much you fire, those bullets just fly in another direction and never hit the psyker's body.

However, the bullets from the "Ostrich" did hit them.

The one who bore witness to that was none other than Tanaka.

"What I was implementing at that time wasn't the probability deviation field. It was my unique ability, the "Coordinate Fixing Zero Point". Fixing the coordinates of a specific object and moving it freely. If I hadn't used that ability, the bullet would have hit me."

That testimony had great meaning.

In other words, the "enemy" possesses weapons that can be used by psychics. He is equipped with a "probability correction bullet" that penetrates the probability deviation field.

"In that case, the logic of the numbers speaks for itself. If all "enemies" were equipped with "probability-modifying bullets" and fired them, even a psyker would quickly turn into a corpse."

"......"

Fushimi's eyebrows deepened at a dangerous angle.

If a single member of "Homura" dies, everything will be over. Suoh Mikoto will never allow it. Even if Las Vegas burns to the ground, even if all US forces turn against him, he will hunt down those who killed his comrades and take revenge.

At that point, Fushimi's mission to "protect world peace" will fail.

"In the end, I guess I have to protect everyone."

When Fushimi said that, Tanaka smiled and nodded.

"It's hard, but that's the way it is. I owe you, Fushimi-san."

Fushimi wanted to throw the coffee at Tanaka. It was originally this guy who brought him to this place. When he opened his mouth trying to say a sarcasm, an alert echoed in the room.

"......!!"

Everyone in the room focused their eyes on the wall monitor.

Sensor W, a device that measures the Weissmann deviation value, reacted to the device that notified when someone passed by. The location is the seventh exit, the northeast exit on the first floor of the casino. Fushimi stared at the monitor, wondering if the enemy had invaded from there, and inadvertently let out a roar of anger.

"What are you doing, Misaki?!"

++++++++++

Anna's energy was fading with each passing day.

It is possible that the Las Vegas tour that she had done was a trap to attract "Homura". The unanimous opinion was that "that sort of thing has nothing to do with us", and Anna probably understood that too. But often understanding and feeling are different creatures.

Another reason for her lack of energy could be that she was physically isolated from Suoh. Currently, the enemy's target is presumed to be "Suoh Mikoto's assassination", and the area around him is said to be the most dangerous place in Las Vegas. Clansmen with combat power aside, Anna and Totsuka were forbidden to get close to Suoh and had to sleep on another floor with an escort.

It seemed that Anna could take it. She was allowed to move freely within the hotel, but she rarely left her room, and she continued to stare dejectedly out the window.

Yata, of course, was the one who displayed his chivalrous spirit.

"Hi, Anna! Since you're here, why don't you take a walk?"

Anna looked at Yata, who said that with wide eyes. Looking out the window, then looking at Yata again, she murmured.

"But that's it."

"Okay! We're here for sightseeing, right? I don't know if it's Mizuchi or Kizuchi, but it's ridiculous to be worried about someone like that!"

Saying that he would blow him up, Yata lifted the book in his hand. It was an information magazine about Las Vegas that she brought from Japan, and there were sticky notes here and there. Anna's face turned bright red.

"That's mine."

"Anna, you wanted to see a lot of different places, didn't you? You put sticky notes on it and wrote various things. So why don't you go where you want to go?"

Anna frowned with concern. She blinked and looked at the information magazine. "I want to go" and "Is it okay to go?" they floated alternately in her mind.

Yata looked around the room and beckoned to everyone who met his eyes. Kamamoto, Bando, Chitose, and Eric, oddly enough, these are the members who traveled to Las Vegas together with Yata.

Yata rested the handle of the mop that he stole the other day on his shoulder and grinned.

"If you're worried, we'll be your escorts! No matter how many hundreds of robots like that come, we'll turn them all to scrap!"

A faint smile finally appeared on Anna's lips when she saw Yata, who reassured her by patting his chest.

Anna reached out and took Yata's information magazine. She must have memorized it by heart, and when she flip through the pages to show it to everyone, there was an article about a gorgeous hotel and fountain show.

"Every day from 18:00 there is a fountain show at Hotel Varangia. I've always wanted to see that."

Yata looked at his watch. 4:47 p.m. The Varangia Hotel was not far away. If they left now, they would be in time for the show.

Yata smiled sheepishly and gave Anna a thumbs up.

"Alright! Then let's go right now!"

Anna held the information magazine close to her chest and nodded slightly but clearly.

++++++++++

Las Vegas and Mikoto Suoh are a very incompatible combination.

Suoh had no interest in money or in casinos. It seemed that he had missed most of the meaning of being in Las Vegas, but on top of that, he paid no attention to the attractions and shows. After visiting Las Vegas, it was just to accompany Anna that he looked around him, not of his own volition.

Therefore, although paradoxical, staying at the "Pyramid" hotel was strangely suitable for Suoh. If he asked for alcohol or cigarettes, they would bring them to him. He would take as many naps as he wanted. Suoh probably didn't pay attention to Tanaka's request not to go out, and Kusanagi and Totsuka painfully realized that he was just staying there because he didn't have to.

If Suoh wanted, he would quickly leave the hotel and spend his time however he wanted.

And if an enemy attacked him, he would easily retaliate.

That was becoming the common opinion of everyone, probably including Tanaka and Fushimi. No one can stop the action of the "Red King". When the time comes, he will go quickly.

And that moment came without warning.

The room where Suoh and the others sleep is a VIP room rented by "Tokijikuin". Luxurious furniture forms a line and of course there is also a bar counter with equipment in the room.

Suoh drank at the rooftop bar only on the first day, other than that, he mainly drank at the bar in his room. Suoh, who was enjoying sake and cigarettes with Kusanagi as the bartender, suddenly looked away from the window, maybe because he was familiar with the taste of HOMRA, or maybe because he got tired of going all the way to the top floor.

"Do you want to hang out?"

He said it in a low voice.

Before Kusanagi could say anything, Suoh grabbed a box of cigarettes and a lighter and stood up. He quickly emptied half of the whiskey that was left in his glass and walked out of the room. Kusanagi panicked and called out to him.

"Wait, Mikoto! Where are you going?"

"A walk."

Suoh's words were short and to the point. Kusanagi exhaled silently, at which point his thoughts had already formed. He shrugged lightly and followed Suoh.

When he came out into the hotel corridor, Fushimi was about to come running.

As soon as he saw Suoh, he flinched and stopped walking. Kusanagi suppressed his laughter at the danger. Did he feel a bit sorry for the change from "Homura" to "Scepter 4"? Suoh didn't mind at all, but his gaze seemed to linger on Fushimi.

Kusanagi gently called out to the tense Fushimi.

"What's wrong, Fushimi? Did something happen?"

"That guy... Yata came out by himself. I'm going to stop him."

"Ah, I don't know what to say, but let's go for a walk too. It's okay."

"Huh?! What are you talking about, Kusanagi-san?"

He ignored Suoh and walk towards the elevator hall. Kusanagi waved his hand lightly on his back and followed him. It wasn't that he didn't feel sorry for Fushimi, but it wasn't something Kusanagi should care about.

After going down to the first floor, the two crossed the lobby with the casino floor on their sides and walked out. The indigo color was beginning to blend into the twilight sky and it would be completely dark in another hour. Suoh and Kusanagi walked through the crowd of tourists with different skin tones.

Suoh's footsteps were in no hurry, and the expression that he was just strolling was perfect. He entered the park and walk while stepping on the shade of the green trees. He stopped at a hot dog cart on the way and bought one with sauce. Kusanagi also bought one.

Before long, Suoh sat down on a bench in the shade of a tree and started eating his hot dog.

Kusanagi did not ask where he was going. He just kept pace with Suoh. If it was just a ride, it was fine. If he had another purpose, he would stick with it. That was it.

As the two of them ate their hot dogs, the darkness of night began to fall.

Suoh, who was licking the sauce off his fingers, suddenly looked up.

At that moment, Kusanagi finally realized it.

There was something there.

After dark, the park was sparsely populated. A jogger passing while listening to music, a family walking home from a casino, a street performer running downtown, but they weren't. There was no one in sight to pay attention to two Asian men lounging on a park bench.

That's what it looked like.

Suoh stood up as people stopped coming and going.

He took a few steps forward with one hand in his pocket. He put his other hand on his neck and punched.

An explosive aura was emitted from Suoh's body.

A storm-like aura that appeared locally blew around Suoh in a radius of about 5 meters. Leaves flew from the trees, trash cans tipped over, and some crashed to the ground with a shorting sound.

Due to the aura that had been displayed in advance, Kusanagi passed through Suoh's "bullying" and left the bench to approach the fallen object.

"What is this?"

He raised his eyebrows and crouched down beside it.

It looked like a disk. It was a shiny silver machine, about 50 cm wide, top mounted.

Judging by the attached propeller, it was probably something like a drone, but...

The strange thing was that the "disk" sometimes became transparent.

Repeatedly turning transparent and non-transparent like a flickering light bulb. Because it broke down due to "Intimidation", it was probably originally floating around them while it was transparent.

Kusanagi stood up and looked at Suoh.

"Is this the guy you were wondering about?"

"Yes. I've been looking at it since yesterday. It's just an eyesore."

Kusanagi was amazed, but at the same time convinced. Even before becoming "King", Suoh possessed the feeling of a wild beast. It would not be strange if he was sensitive to the gaze of an invisible "something".

But other than that,

"You know. Say that beforehand and then move on."

"Even if you say so, it can't be helped. The quickest way is to destroy it."

"Are you a barbarian?"

When he picked up the broken "disc", it was much lighter than it appeared. It was probably a companion to the "supernatural weapon" that Totsuka and others were talking about. They said they were attacked by an ostrich-like bipedal robot, but because this "disc" has a camera in its eye, it could be a reconnaissance weapon equipped with stealth capabilities.

When...

The "disk" stopped becoming transparent.

At the same time, the eye chamber lit up red. Chi-chi-chi-chi--, while blinking at short intervals, it gradually became faster, like when a creature's heartbeat speeds up when it dies.

A bad feeling ran down his spine.

At this time, Suoh's toes were already lifting the "disc".

The "disc" jumped almost at a right angle to the ground, spinning in the air, it spread flashy flames and exploded grandly.

"Uh...!"

Kusanagi quickly covered his face with his arms to block the blast. Burnt electronics and screws rained down over their heads, falling into the fountains and creating small columns of water.

After letting it go, Kusanagi cursed.

"It even has a self-destruct function! What a dangerous thing...!"

"You are too careless."

He glared at Suoh who said that rudely, but there was no room for objection. The enemy was trying to kill them. Even if it was a reconnaissance weapon, it should have been expected to be loaded with weapons.

That said, Kusanagi also had something to say.

"No way, I thought of giving it to the "Tokijikuin" people. I thought if those people could analyze it, it would be a bit more advantageous."

Since it self-destructed, there was nothing that could be done to give it to them. Kusanagi sighed, Suoh smiled only at the corner of his mouth.

"Then don't be busy from now on."

"What?"

Right after Kusanagi asked again...

Three pieces of iron fell from the sky.

With an earth-shattering roar, the hunk of iron landed and aimed its red-eyed camera at Suoh and Kusanagi. It was a special weapon with the same shape as the one that chased Totsuka and the others, with bipods and a machine gun: "Ostrich".

They formed a triangle centered on Suoh and Kusanagi, stepping at exactly the same time with their feet of steel.

The machine gun began to rotate slowly. While all three guns were pointed at him, Suoh muttered in his usual tone.

"There's no shortage of things to do with analysis, right?"

Suoh's smile turned into a fierce one, like a lion facing prey from him.

++++++++++

The restaurant "Fontaine" was full of people.

The Varangia Hotel's fountain show has become one of the specialties of Las Vegas for its magnificent and beautiful performance. The show itself is free, but if you want to take your time and enjoy the food, you'll have to go to a restaurant affiliated with the hotel, like Fontaine, and order incredibly expensive dishes.

Right now, Yata and the others are in danger. Even if you ignore the fact that it's tourist area pricing, the amount of money Yata can live on for three days is tiered on the menu. Kamamoto, Chitose, Bando, and Eric craned their necks and looked at the menu, frowning.

Anna, who was sitting in front of them, said anxiously.

"Misaki? I'm not hungry..."

At the same time, Yata and the others turned their faces away from the menu and smiled widely.

"No, what are you talking about Anna? Order whatever you want, I'm sure everything is delicious!"

"No..."

Receiving the menu in confusion, Anna began to read it. Meanwhile, Yata and the others put their foreheads together.

"Hey. How much do you have now?"

"About $50. I didn't expect it to be this expensive..."

"Ok. Let's go ask Anna what she likes and we'll stick with the cheapest soup."

"Hey! Does the ribeye here look that good?!"

"Kamamoto, you are noisy. There is no money to buy something like that. Drink water from the fountain and bear it."

Fortunately, Anna did not find out about the unfortunate conversation they exchanged in secret. She lowered the menu and pointed to a large photo with glowing eyes.

"Can I order this Las Vegas Raspberry Night Parfait?"

The majestic parfait, like an imposing tower, has an incredible price of $45.

If this was a tourist spot in Japan, and if Anna wasn't in front of them, he would have called the store clerk, he would have grabbed him by the lapels, cursed at him, and left the store. Yata desperately resisted the urge and smiled.

"Oh! Is that enough? Don't stop!"

Anna gently shook her head.

"Maybe I can't eat it alone. Shall we eat together?"

"Anna..."

Kamamoto was the only one who was touched by her kindness, and Yata and the others looked down embarrassed that the girl had seen through their financial situation, well apart from that the fountain show started as soon as they brought all dishes.

"Wow...!"

Even Yata, who had been complaining in his heart about it, couldn't help but look at it with wide eyes.

Water and light emerged from the center of a large fountain that looked like a small baseball field. The start was smooth, and with the magnificent classical music, the thread-thin water began to spin in a spiral.

Under the indigo sky, the lights buried under the surface of the water gracefully illuminated the splendidly dancing column of water. White, blue, and red, the lights that shone in various colors were like water fairies dancing in lustrous dresses.

Anna, who was watching the show, muttered.

"Beautiful red."

Yata looked at Anna for a moment, then smiled and nodded. Anna wasn't looking at him, but seeing her reassuring profile made him very happy to be there.

Classical music gradually rose. At the same time, the water column was thick and large, changing the atmosphere from elegant to majestic. A mist of water rose up for a moment, and as if blowing on it, a gigantic column of water came out of the water. A water fairy that danced gracefully and sweetly suddenly turned into a majestic and spectacular water giant, that was the impression he got.

The surrounding tourists also let out sighs and cheers as they admired the magnificent view.

Among them, Yata noticed a man approaching.

Dark skin and deeply chiseled features. A black biker jacket worn directly on bare skin and black gloves. Seeing that cheerful smile on his lips, Yata involuntarily raised his voice.

"You are Ed! What a coincidence, to find yourself in a place like this!"

"Oh, Misaki! Long time no see! Looks like you've been doing well!"

Anna, Kamamoto and the others seemed to have noticed Ed's presence, smiling and opening their arms. Seeing the tall Latin American man, Kamamoto and the others bowed their heads, and Anna's face suddenly stiffened.

Yata did not notice that. Laughing, he introduced Ed.

"This boy is Ed! He picked me up in the desert. He saved my life!"

"Hahahaha! Misaki, you're exaggerating! When you're in trouble, you can ask for help even in Japan, right? I just did the obvious!"

"Well, I don't really know, but… well, sit down."

"Oh, thank you! Then don't hesitate!"

Ed sat down in a chair and crossed his legs. Yata noticed that Anna had subtly distanced herself from him.

"By the way, what's up, Ed? Are you a tourist too?"

"Hmm, no, you're wrong. I'm not saying it's a job, but, I'm looking for someone. I thought Misaki would know."

"Huh? Why me?"

Ed said with a smile on his face.

"Mikoto Suoh. Where is he?"

Everyone was speechless as they looked at Ed.

Ed put his arms on the table and poked his face out. With an innocent tone like a child begging for something.

"The same hotel as Misaki and the others? So can you tell me where you're staying? In exchange, I'll let Misaki and the others go."

"You..."

The show reached its climax. The column of water erupted with even greater magnificence, and the light shone in various ways to color it. Catching the kaleidoscope of water and light out of the corner of his eye, however, what came out of Yata's mouth was a dry voice.

"A servant of Mizuchi?"

He could feel Kamamoto, Bando, Chitose and Eric getting nervous. Ed made a rather surprised face and waved his hands exaggeratedly.

"No, no! I don't care about that guy! The only person I want to see is Mikoto Suoh!"

"What are you going to do when you meet Mikoto-san?"

Ed gave a short answer with a fierce smile that was probably his true nature.

"I will kill him."

Anna's body trembled. As he looked at her with rather affectionate eyes, Ed said:

"There is only one "Red King". I can't do anything more than that. So I will kill him. Get rid of this world. If I don't do that, I won't be able to fight everyone either."

They had no idea what he was talking about.

But there was no need to understand. There was only one thing to understand.

In other words, this guy is an enemy.

"I have nothing to teach you."

Ed showed no disappointment at his hostile words. "Hmm," he murmured, leaning his long back against the seatback and looking up at the ceiling.

"So if I kill them, will Mikoto Suoh show up too?"

(Try it!)

Just as he was about to say that, an angry voice resounded.

"Misaki! What are you doing?!"

Confused, he inadvertently looked away from Ed.

Fushimi was standing at the entrance of the store. With an angry expression on his face, he approached them with long strides while several "Tokijikuin" agents followed him. Ed looked at that too.

His blue eyes widened.

Fushimi didn't care about that, he walked over to the table and slapped him. He muttered under his breath while throwing an angry look at Yata.

"Don't leave the hotel, I've told you over and over again...! Has your brain degenerated to the point where you can't protect yourself? When idiots get along with each other, isn't it just that idiots move and become more and more idiotic?"

"...Saru. Now..."

"Hurry up and get out. Let's go back to the hotel. Even if it's Miko-..., Suoh Mikoto went and ruined it. I don't have time to take care of you."

Yata started and looked back at Fushimi. Just as he was about to ask him what it was that had messed up...

"Puff."

Suddenly, such a voice resounded.

"Hahahahahahahahahahahahahahaha!"

It was Ed.

Leaning down, holding his stomach, he laughed as if he couldn't stand it. He slammed the table with his black-gloved right hand, and the parfait fell on impact and spilled onto the floor.

The surrounding tourists shifted their gaze from the show to Ed, wondering what was going on. But he didn't care about those things, and he was spreading laughter.

"Hahahahahahahahahahaha! What is this? Why do the blue and red clans get along so well? Huh?! What kind of joke is this?!"

Yata did not understand the cries in English. But there was something he could understand.

This man was not laughing, only his unbearable hatred and anger burst into laughter.

Dazzling eyes with murderous intent pierced Yata head-on.

"How stupid! These bastards are from the Red Clan?! What else? Taking a girl and eating a parfait, there's no way I can accept the bastards who are good friends with the blue clothes!"

Flames erupted from Ed's right hand.

An oddly long arm-shaped flame that rose high enough to lick the ceiling shattered the table with its force, scattering shards and sparks throughout the restaurant.

#k #k project #k las vegas #suoh mikoto #kusanagi izumo #totsuka tatara #yata misaki #homra #k stories #k-project #fushimi saruhiko #scepter 4

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