Connect with us

Uncategorized

Tracing the Evolution of Foundational AGI Theories | IDOs News

Avatar

Published

on

Tracing the Evolution of Foundational AGI Theories | IDOs News




Jessie A Ellis
Aug 02, 2024 06:50

Explore the historical development and core theories of Artificial General Intelligence (AGI), from Turing’s early concepts to modern advancements.





The dream of Artificial General Intelligence (AGI), a machine with human-like intelligence, is something that can be traced back to early computational theories in the 1950s. Pioneers like John von Neumann explored the possibilities of replicating the human brain’s functions. Today, AGI represents a paradigm shift from the narrow AI tools and algorithms that excel at specific tasks to a form of intelligence that can learn, understand, and apply its knowledge across a wide range of tasks at or beyond the human level.

While the precise definition of AGI is not broadly agreed upon, it generally refers to an engineered system capable of:

  • Displaying human-like general intelligence;
  • Learning and generalizing across a wide range of tasks;
  • Interpreting tasks flexibly in the context of the world at large.

The journey to AGI has been marked by numerous theories and conceptual frameworks, each contributing to our understanding and aspirations of this revolutionary technology.

Earliest Conceptualizations of AGI

Alan Turing’s seminal paper, “Computing Machinery and Intelligence” (1950), introduced the idea that machines could potentially exhibit intelligent behavior indistinguishable from humans. The Turing Test, which evaluates a machine’s ability to exhibit human-like responses, became a foundational concept, emphasizing the importance of behavior in defining intelligence. John von Neumann’s book, “The Computer and the Brain” (1958), explored parallels between neural processes and computational systems, sparking early interest in neurocomputational models.

Symbolic AI and Early Setbacks

In the 1950s and 60s, Allen Newell and Herbert A. Simon proposed the Physical Symbol System Hypothesis, asserting that a physical symbol system has the necessary and sufficient means for general intelligent action. This theory underpinned much of early AI research, leading to the development of symbolic AI. However, by the end of the 1960s, limitations of early neural network models and symbolic AI became apparent, leading to the first AI winter in the 1970s due to reduced funding and interest.

Neural Networks and Connectionism

In the 1980s, a resurgence in neural network research occurred. The development and commercialization of expert systems brought AI back into the spotlight. Advances in computer hardware provided the necessary computational power to run more complex AI algorithms. The backpropagation algorithm, developed by David Rumelhart, Geoffrey Hinton, and Ronald Williams, enabled multi-layered neural networks to learn from data, effectively training complex models and rekindling interest in connectionist approaches to AI.

John Hopfield introduced Hopfield networks in 1982, and Geoffrey Hinton and Terry Sejnowski developed Boltzmann machines between 1983 and 1985, further advancing neural network theory.

The Advent of Machine Learning and Deep Learning

Donald Hebb’s principle, summarized as “cells that fire together, wire together,” laid the foundation for unsupervised learning algorithms. Finnish Professor Teuvo Kohonen’s self-organizing maps in 1982 showed how systems could self-organize to form meaningful patterns without explicit supervision. The ImageNet breakthrough in 2012, marked by the success of AlexNet, revolutionized the field of AI and deep learning, demonstrating the power of deep learning for image classification and igniting widespread interest and advancements in computer vision and natural language processing.

Cognitive Architectures and Modern AGI Research

Cognitive architectures like SOAR and ACT-R emerged in the 1980s as comprehensive models of human cognition, aiming to replicate general intelligent behavior through problem-solving and learning. Theories of embodied cognition in the 1990s emphasized the role of the body and environment in shaping intelligent behavior. Marcus Hutter’s Universal Artificial Intelligence theory and the AIXI model (2005) provided a mathematical framework for AGI.

One of the significant developments in AGI theory is the creation of OpenCog, an open-source software framework for AGI research founded by Ben Goertzel in 2008. OpenCog focuses on integrating various AI methodologies to create a unified architecture capable of achieving human-like intelligence. Efforts to integrate neural and symbolic approaches in the 2010s aimed to combine the strengths of both paradigms, offering a promising pathway toward AGI.

Current Frontiers in AI & AGI

In the 2020s, foundation models like GPT-3 have shown initial promise in text generation applications, displaying some cross-contextual transfer learning. However, they are still limited in full-spectrum reasoning, emotional intelligence, and transparency. Building on the foundations of OpenCog Classic, OpenCog Hyperon represents the next generation of AGI architecture. This open-source software framework synergizes multiple AI paradigms within a unified cognitive architecture, propelling us toward the realization of human-level AGI and beyond.

According to SingularityNET (AGIX), Dr. Ben Goertzel believes that AGI is now within reach and likely to be achieved within the next few years. He emphasizes the importance of keeping the deployment of AGI decentralized and the governance participatory and democratic to ensure that AGI will grow up to be beneficial to humanity.

As we continue to push the boundaries with large language models and integrated cognitive architectures like OpenCog Hyperon, the horizon of AGI draws nearer. The path is fraught with challenges, yet the collective effort of researchers, visionaries, and practitioners continues to propel us forward. Together, we are creating the future of intelligence, transforming the abstract into the tangible, and inching ever closer to machines that can think, learn, and understand as profoundly as humans do.

Image source: Shutterstock



Continue Reading
Click to comment

Leave a Reply

Your email address will not be published. Required fields are marked *

Uncategorized

Advantages of Mobile Apps in Gambling: The Example of Pin Up App | IDOs News

Avatar

Published

on

Advantages of Mobile Apps in Gambling: The Example of Pin Up App | IDOs News


By Terry Ashton, updated August 31, 2024

Online gambling is going mobile — over 50% of players are already playing casino games on their mobile devices, and their number is expected to grow further. But does a mobile app have actual advantages over browser-based play? We decided to do more profound research by accessing and trying gambling on a desktop browser, mobile browser, and the app. That allowed us to distinguish casino mobile applications’ key benefits and drawbacks. If you’re considering using one, just keep reading — we will share some helpful insights below. 

Benefits of Mobile Play at Pin Up Casino

The rise of online gambling happens for multiple reasons, including the following ones: 

  • Ultimate accessibility. You can access the app anywhere, even on the go. You don’t need to take additional actions — the casino opens with just one click. 
  • Lower Internet requirements, offline play. If you play for fun, you can do it even without an Internet connection. If you prefer to play real money, the requirements for an Internet connection will still be much lower because most data is already downloaded to your device. 
  • Push notifications. You can immediately learn about the new top promotions and the hottest games without checking your email. 
  • Special bonuses. Sometimes, special bonuses are granted to mobile players. Some casinos may add them occasionally to encourage players to play on apps. 
  • The same game selection. If a casino is modern and cooperates with top providers, all games will be compatible with mobile devices. For instance, if you play at Pin Up casino online, you can access the same collection of games. That goes not only for slots but also for live games, table games, etc. 
  • Higher security standards. The app is protected even better than the site. Data is encrypted, and the chance that anyone will access your account is close to zero. 

Registration also goes smoothly. Once you sign up on the browser or app, you can access the platform with just one click by entering your Pin Up login and password. 

Considering the Cons: Potential Drawbacks of Using a Pin-Up Mobile App 

Nothing is perfect, and neither are casino apps. Gamblers should also consider the drawbacks, and the most common ones are as follows: 

  • Installing software is a must. You need to install the software on your phone. It’s safe if it’s the official casino site and a good product. However, clicking on the wrong link and downloading the wrong APK file may result in problems. 
  • Battery drain and storage space. It’s no secret that charging the phone all the time is annoying, and innovative slots with top graphics may drain your battery quickly. Also, though most apps don’t take much space (in the case of Pin Up, it’s just about 100 Mb), they still require more effort to manage it. 
  • Compatibility requirements. Any app will have technical requirements, and most aren’t compatible with old mobile devices and tablets. Also, you’ll need to install updates quite regularly. 
  • Smaller screen. This is a disadvantage for those who prefer playing on larger screens, particularly those who prefer live dealer games. 

Do the pros outweigh the cons for you? If yes, the mobile app will boost your experience. If not, browser play may be a better option. 

Final Thoughts: The App vs. Browser Play at Pin-Up Casino

Technology is shaping the industry. Nowadays, there’s no such significant difference between playing on a mobile app and a mobile or desktop browser. You get the same game selection, the same bonuses, and the same smooth experience. So, it’s a matter of taste. Choose what will work best for you and enjoy your play.


Continue Reading

Uncategorized

NVIDIA Introduces Fast Inversion Technique for Real-Time Image Editing | IDOs News

Avatar

Published

on

NVIDIA Introduces Fast Inversion Technique for Real-Time Image Editing | IDOs News




Terrill Dicki
Aug 31, 2024 01:25

NVIDIA’s new Regularized Newton-Raphson Inversion (RNRI) method offers rapid and accurate real-time image editing based on text prompts.





NVIDIA has unveiled an innovative method called Regularized Newton-Raphson Inversion (RNRI) aimed at enhancing real-time image editing capabilities based on text prompts. This breakthrough, highlighted on the NVIDIA Technical Blog, promises to balance speed and accuracy, making it a significant advancement in the field of text-to-image diffusion models.

Understanding Text-to-Image Diffusion Models

Text-to-image diffusion models generate high-fidelity images from user-provided text prompts by mapping random samples from a high-dimensional space. These models undergo a series of denoising steps to create a representation of the corresponding image. The technology has applications beyond simple image generation, including personalized concept depiction and semantic data augmentation.

The Role of Inversion in Image Editing

Inversion involves finding a noise seed that, when processed through the denoising steps, reconstructs the original image. This process is crucial for tasks like making local changes to an image based on a text prompt while keeping other parts unchanged. Traditional inversion methods often struggle with balancing computational efficiency and accuracy.

Introducing Regularized Newton-Raphson Inversion (RNRI)

RNRI is a novel inversion technique that outperforms existing methods by offering rapid convergence, superior accuracy, reduced execution time, and improved memory efficiency. It achieves this by solving an implicit equation using the Newton-Raphson iterative method, enhanced with a regularization term to ensure the solutions are well-distributed and accurate.

Comparative Performance

Figure 2 on the NVIDIA Technical Blog compares the quality of reconstructed images using different inversion methods. RNRI shows significant improvements in PSNR (Peak Signal-to-Noise Ratio) and run time over recent methods, tested on a single NVIDIA A100 GPU. The method excels in maintaining image fidelity while adhering closely to the text prompt.

Real-World Applications and Evaluation

RNRI has been evaluated on 100 MS-COCO images, showing superior performance in both CLIP-based scores (for text prompt compliance) and LPIPS scores (for structure preservation). Figure 3 demonstrates RNRI’s capability to edit images naturally while preserving their original structure, outperforming other state-of-the-art methods.

Conclusion

The introduction of RNRI marks a significant advancement in text-to-image diffusion models, enabling real-time image editing with unprecedented accuracy and efficiency. This method holds promise for a wide range of applications, from semantic data augmentation to generating rare-concept images.

For more detailed information, visit the NVIDIA Technical Blog.

Image source: Shutterstock



Continue Reading

Uncategorized

AMD Radeon PRO GPUs and ROCm Software Expand LLM Inference Capabilities | IDOs News

Avatar

Published

on

AMD Radeon PRO GPUs and ROCm Software Expand LLM Inference Capabilities | IDOs News




Felix Pinkston
Aug 31, 2024 01:52

AMD’s Radeon PRO GPUs and ROCm software enable small enterprises to leverage advanced AI tools, including Meta’s Llama models, for various business applications.





AMD has announced advancements in its Radeon PRO GPUs and ROCm software, enabling small enterprises to leverage Large Language Models (LLMs) like Meta’s Llama 2 and 3, including the newly released Llama 3.1, according to AMD.com.

New Capabilities for Small Enterprises

With dedicated AI accelerators and substantial on-board memory, AMD’s Radeon PRO W7900 Dual Slot GPU offers market-leading performance per dollar, making it feasible for small firms to run custom AI tools locally. This includes applications such as chatbots, technical documentation retrieval, and personalized sales pitches. The specialized Code Llama models further enable programmers to generate and optimize code for new digital products.

The latest release of AMD’s open software stack, ROCm 6.1.3, supports running AI tools on multiple Radeon PRO GPUs. This enhancement allows small and medium-sized enterprises (SMEs) to handle larger and more complex LLMs, supporting more users simultaneously.

Expanding Use Cases for LLMs

While AI techniques are already prevalent in data analysis, computer vision, and generative design, the potential use cases for AI extend far beyond these areas. Specialized LLMs like Meta’s Code Llama enable app developers and web designers to generate working code from simple text prompts or debug existing code bases. The parent model, Llama, offers extensive applications in customer service, information retrieval, and product personalization.

Small enterprises can utilize retrieval-augmented generation (RAG) to make AI models aware of their internal data, such as product documentation or customer records. This customization results in more accurate AI-generated outputs with less need for manual editing.

Local Hosting Benefits

Despite the availability of cloud-based AI services, local hosting of LLMs offers significant advantages:

  • Data Security: Running AI models locally eliminates the need to upload sensitive data to the cloud, addressing major concerns about data sharing.
  • Lower Latency: Local hosting reduces lag, providing instant feedback in applications like chatbots and real-time support.
  • Control Over Tasks: Local deployment allows technical staff to troubleshoot and update AI tools without relying on remote service providers.
  • Sandbox Environment: Local workstations can serve as sandbox environments for prototyping and testing new AI tools before full-scale deployment.

AMD’s AI Performance

For SMEs, hosting custom AI tools need not be complex or expensive. Applications like LM Studio facilitate running LLMs on standard Windows laptops and desktop systems. LM Studio is optimized to run on AMD GPUs via the HIP runtime API, leveraging the dedicated AI Accelerators in current AMD graphics cards to boost performance.

Professional GPUs like the 32GB Radeon PRO W7800 and 48GB Radeon PRO W7900 offer sufficient memory to run larger models, such as the 30-billion-parameter Llama-2-30B-Q8. ROCm 6.1.3 introduces support for multiple Radeon PRO GPUs, enabling enterprises to deploy systems with multiple GPUs to serve requests from numerous users simultaneously.

Performance tests with Llama 2 indicate that the Radeon PRO W7900 offers up to 38% higher performance-per-dollar compared to NVIDIA’s RTX 6000 Ada Generation, making it a cost-effective solution for SMEs.

With the evolving capabilities of AMD’s hardware and software, even small enterprises can now deploy and customize LLMs to enhance various business and coding tasks, avoiding the need to upload sensitive data to the cloud.

Image source: Shutterstock



Continue Reading

Trending