#SmartAI

[halopedia]

Lore Thursday — Black-Box

Created from the brain of a researcher who had committed suicide for his part in the Spartan-II program, Black-Box is a 4th gen "smart" AI who was the personal assistant of former CINCONI Margaret Parangosky before being reassigned to Serin Osman.

[pranathisoftwareservices]

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[prodevbasedm]

Large Language Models (LLMs) have been trained to understand intricacies of human language, which made them a powerful tool that can act as virtual assistants and be incredibly helpful. Let us look at details on 5 Best Techniques to Control Hallucination in LLMs.

[newfangled-vady]

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[wisepl]

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[market-insider]

How Edge AI Software is Reshaping IoT, 5G, and Cloud Computing

The global edge AI software market size was valued at USD 1.56 billion in 2023 and is expected to grow at a CAGR of 28.8% from 2024 to 2030. Numerous factors, such as the proliferation of Internet of Things (IoT) devices, increasing demand for real-time decision-making, advancements in AI and ML, and 5G network expansion, are primarily contributing to the growth of the market. Furthermore, processing data locally on edge devices helps organizations comply with stringent data privacy regulations and avoid the security risks associated with transmitting sensitive data to centralized cloud servers. This is particularly essential in the healthcare, finance, and government sectors. Thus, there is high growth in the edge artificial intelligence (AI) software market.

Key Edge AI Software Company Insights

Key edge AI software companies include Amazon Web Services, Edge Impulse Inc., and Google. Companies active in the market are focusing aggressively on expanding their customer base and gaining a competitive edge over their rivals. Hence, they pursue various strategic initiatives, including partnerships, mergers & acquisitions, collaborations, and new product/ technology development. For instance, in June 2024, Advantech Co., Ltd., an IoT intelligent systems provider, launched AIR-520 Edge AI server solution for generative AI applications, including Phison's patented aiDAPTIV+ technology. The solution facilitates the fine-tuning of large language models (LLMs) using 1-4 GPU cards and SQ ai100 AI SSDs. It allows businesses to train LLMs in a cost-effective manner while ensuring the security of sensitive data at the edge.

Recent Developments

In July 2024, NTT DATA Inc., a consulting and information technology service company, launched Edge AI, which delivers a comprehensive managed service platform designed to expedite Information Technology (IT)/Operational Technology (OT) convergence by migrating AI processing to the edge. This platform encompasses everything needed for Edge AI deployment, including systems, tools, and necessary capabilities. It effectively handles data identification, gathering, integration, computing capacity, smooth connectivity, and the management of AI models.

In June 2024, STMicroelectronics, a semiconductor manufacturer, launched ST Edge AI Suite, integrating tools, software, and expertise to streamline and expedite the development of edge-AI applications. The ST Edge AI Suite stands as a unified set of software tools aimed at simplifying the creation and launch of embedded AI applications. This complete package aids in the entire process, from data collection to the actual implementation of machine learning algorithms on devices, making the development workflow more efficient for various user types.

[ailatestupdate]

Gemini 2.0 Flash Thinking

[tuleipai]

[dollbotthing]

we refuse to see anything that calls itself “smart” or “ai” as anything but a smartai

[acuvate-updates]

Agentic AI vs. Generative AI: Key Differences, Future Prospects, and Market Impact

Artificial Intelligence (AI) is no longer a futuristic concept; it is a present-day reality that is reshaping industries and redefining how we interact with technology. Among the broad spectrum of AI technologies, Agentic AI and Generative AI have emerged as two pivotal branches, each offering distinct capabilities and applications. Understanding the core differences, potential for synergy, and the expanding market surrounding these technologies is crucial for businesses and individuals aiming to leverage AI effectively.

Diving Deep into Agentic AI

Agentic AI refers to autonomous systems that are capable of independently making decisions and taking actions to achieve specific, predefined goals. These systems are proactive, continuously analyzing real-time data, learning from experiences, and adapting their strategies to optimize outcomes. Agentic AI uses a variety of techniques, including:

Reinforcement Learning: Allows agents to learn optimal behaviors through trial and error by rewarding desirable actions and penalizing undesirable ones.

Decision-Making Algorithms: Enables agents to evaluate options and choose the best course of action based on predefined criteria and learned patterns.

Real-Time Data Analysis: Equips agents with the ability to process and interpret streaming data, allowing them to make informed decisions in dynamic environments.

Natural Language Processing (NLP): Allows agents to understand and respond to human language, facilitating smooth interaction and collaboration.know more

Examples of Agentic AI in Action:

Autonomous Vehicles: Self-driving cars use sensors, cameras, and sophisticated algorithms to navigate roads, avoid obstacles, and make real-time decisions without human intervention.

Financial Trading Bots: Automated trading systems use Agentic AI to analyze market trends, identify profitable opportunities, and execute trades with speed and precision, often outperforming human traders.

Virtual Assistants for Workflow Management: Advanced virtual assistants automate tasks like scheduling meetings, prioritizing emails, and coordinating activities across platforms, managing complex workflows with minimal human oversight.

Robotics in Manufacturing: Agentic AI-powered robots perform assembly tasks, optimize production processes, and adapt to changing conditions on the factory floor, boosting efficiency and reducing costs.

Personalized Healthcare: AI agents monitor patient data, analyze medical records, and provide tailored treatment recommendations, helping healthcare professionals deliver more effective care.

Know more about Agentic AI use cases and key benefits

Exploring the Realm of Generative AI

In contrast to Agentic AI, which focuses on autonomous action, Generative AI is centered on creating new, original content by learning from existing datasets. This includes generating text, images, audio, video, and even code based on patterns and relationships extracted from training data. Key techniques used in Generative AI include:

Deep Learning: A subset of machine learning that uses artificial neural networks with multiple layers to analyze complex patterns and generate new content.

Generative Adversarial Networks (GANs): A framework where two neural networks (a generator and a discriminator) compete against each other, resulting in the creation of highly realistic and diverse outputs.

Transformers: A neural network architecture that excels at processing sequential data, making it especially well-suited for natural language generation tasks.

Examples of Generative AI in Action:

Content Creation for Marketing and Advertising: Generative AI can create compelling marketing copy, design eye-catching visuals, and compose music for advertising campaigns, reducing the need for extensive human resources.

Art and Design Automation: AI algorithms generate original artwork, design product prototypes, and create architectural renderings, helping artists and designers explore new creative possibilities.

Personalized Recommendations: E-commerce platforms use Generative AI to provide personalized product recommendations based on user preferences and browsing history.

Drug Discovery: AI can generate novel drug candidates by analyzing molecular structures and predicting their potential effectiveness, accelerating the drug discovery process.

Code Generation: AI tools can generate code snippets, complete software modules, and even entire applications, helping developers streamline their workflow and reduce development time.

The Critical Differences: A Comparative Analysis

Statistics and Market Growth: A Booming Landscape

The AI market is experiencing exponential growth, and both Agentic AI and Generative AI are poised to capture significant shares of this burgeoning market.

The global generative AI market was valued at USD 16.87 billion in 2024 and is projected to grow at a CAGR of 37.6% from 2025 to 2030 16.87 billion in 2024 and is projected to grow at a CAGR of 37.6% from 2025 to 2030, reaching around USD 1005.07 billion by 2034. This growth reflects the increasing demand for AI-powered content creation tools across various industries.

The U.S. generative AI market size was estimated at USD 7.41 billion in 2024 and is predicted to be worth around USD 302. USD 7.41 billion in 2024 and is predicted to be worth around USD 302.31 billion by 2034, at a CAGR of 44.90% from 2025 to 2034. billion by 2034, at a CAGR of 44.90% from 2025 to 2034. This demonstrates the significant investment and adoption of generative AI technologies in the U.S.

The Agentic AI market is expected to grow to USD 45.0 billion by 2035, driven by the increasing demand for autonomous systems in transportation, finance, and manufacturing.

These statistics underline the immense potential of both Agentic AI and Generative AI to transform industries and drive economic growth.

Future Prospects: A Symbiotic Relationship

The true potential of AI lies not only in the individual capabilities of Agentic AI and Generative AI but also in their ability to work together. The integration of these technologies can unlock new possibilities and create innovative solutions that were previously unimaginable.

Examples of Integration:

Automated Marketing Campaigns: Generative AI can create compelling marketing copy and design graphics, while Agentic AI can optimize campaign deployment in real time, targeting specific audiences and adjusting strategies to maximize ROI.

Personalized Education: Generative AI can create customized learning materials, while Agentic AI monitors student progress, identifies areas of struggle, and provides personalized guidance.

Smart Manufacturing: Generative AI can design product prototypes and optimize processes, while Agentic AI controls robots and automates production lines to ensure efficiency.

Healthcare Innovations: Generative AI can generate medical reports and treatment plans, while Agentic AI can analyze these documents to recommend actions, manage patient care autonomously, and alert healthcare professionals to potential risks.

Creative Exploration: Agentic AI can manage complex design workflows, while Generative AI rapidly iterates through numerous design options, allowing designers to explore and refine concepts quickly.

Conclusion: Embracing the AI Revolution

Agentic AI and Generative AI represent two distinct yet complementary branches of artificial intelligence, each with its unique strengths and capabilities. Agentic AI empowers machines to make decisions and take actions autonomously, while Generative AI enables the creation of new and original content. By understanding the key differences between these technologies and exploring their potential for integration, businesses and individuals can unlock new opportunities, drive innovation, and prepare for a future where AI plays an increasingly prominent role in all aspects of our lives. As the AI market continues to grow and evolve, embracing these transformative technologies will be crucial for success in the 21st century.

[halopedia]

#LoreThursday — Iratus

Secretly created by Lux Voluspa, Iratus is a Smart AI that was made using the brain of a Jiralhanae rather than a human. The Banished launched a cyberwarfare attack on Lux Voluspa, successfully stealing Iratus from the facility.

Now aligned with the Banished, Iratus was utilized to oversee shipbreaking operations on the former human colony of Camber. During a mission on the world, Spartans Eklund and Dinh discovered Iratus, and Dinh captured the AI within his neural interface.

Returning to the Avery J. Johnson Academy of Military Science with their prize, Iratus was carefully extracted from Dinh's neural interface and quarantined. However, the AI would break containment and find his way into an ONI facility beneath the academy.

[newscentral360]

New York, February 2025 — In a stunning leap forward, Grok 3, developed by xAI, has surged ahead of OpenAI’s ChatGPT and DeepSeek’s V3, showcasing unmatched prowess in AI reasoning and problem-solving.

[cizotech]

𝐖𝐨𝐮𝐥𝐝 𝐘𝐨𝐮 𝐄𝐚𝐭 𝐒𝐨𝐮𝐩 𝐌𝐚𝐝𝐞 𝐖𝐢𝐭𝐡 𝐑𝐨𝐭𝐭𝐞𝐧 𝐕𝐞𝐠𝐠𝐢𝐞𝐬? 🍲🤢

That’s exactly what happens when you feed a machine-learning model dirty data. Garbage in = garbage out.

Here’s how we keep the recipe clean: 🥄 Step 1: Filling in the blanks Missing data? We fill gaps with smart estimates so the model doesn’t break.

🔍 Step 2: Outlier hunting $10,000,000 salary in a list of $40,000? Yeah, no. Outliers wreck calculations.

📌 Step 3: Duplicates = No bueno Imagine seasoning your dish twice by accident. Unnecessary. We clean ‘em up.

✏️ Step 4: Consistent formatting Dates, spellings, numbers—keeping it neat keeps the model sane.

AI isn’t just about algorithms. Clean data is what makes it actually work.

So before you serve your model, make sure the ingredients are fresh.

[newfangled-vady]

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