#aiprojects

[generativeaimasters]

🌟 Hear it from our learners! 🌟 Rajesh Patel shares his experience at Generative AI Masters, where our comprehensive curriculum, flexible learning options, and hands-on real-time projects have helped him grasp generative AI concepts and apply them effectively. Join us to unlock your potential in the world of AI! 🚀

[wisepl]

Data Annotation for Stone Segmentation

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[shuttech]

Raspberry Pi Introduces Camera Module Designed for Vision-Based AI

Raspberry Pi has unveiled its latest innovation: a specialized Camera Module designed to enhance vision-based AI applications. This new module boasts advanced features, including high-resolution imaging and improved low-light performance, making it an ideal choice for projects that require reliable visual input. With seamless integration into existing Raspberry Pi systems, developers can leverage the module to create smart cameras, robotics, and IoT devices powered by machine learning algorithms.

https://shuttech.com/technology/raspberry-pi-introduces-camera-module-designed-for-vision-based-ai/

[sayeedxp]

𝗔 𝗿𝗲𝗰𝗲𝗻𝘁 𝗮𝗰𝗰𝗼𝗺𝗽𝗹𝗶𝘀𝗵𝗺𝗲𝗻𝘁 𝗜'𝗺 𝗽𝗮𝗿𝘁𝗶𝗰𝘂𝗹𝗮𝗿𝗹𝘆 𝗽𝗿𝗼𝘂𝗱 𝗼𝗳 𝗳𝗿𝗼𝗺 2023...

My team and I at @TechLookBD had the incredible opportunity to work on a groundbreaking project for Scale AI, contributing to the development of Google's Gemini AI.

This small contribution yet significant milestone for us made possible for @Mahbubur Rahman who lead this project. Over the course of the year, he dedicated approximately 5,000+ hours to this endeavor.

Reflecting on this achievement, I'm reminded of the dedication, teamwork, and innovation required to contribute to such a groundbreaking project in the AI field.

The experience not only enhanced our technical skills but also demonstrated the power of collaboration in pushing the boundaries of what's possible in artificial intelligence.

Working on a project of this magnitude has also underscored the importance of harnessing technology to drive future advancements and solve real-world problems.

What milestones are you proud of in your professional journey?

#AI #Innovation #Teamwork

[govindhtech]

At ICML 2024, Consider Machine Learning’s Social Impacts

Observing Silicon  AI researchers have strong social concerns. Scientists at ICML 2024 are split between exploring the limits of machine learning and considering the social implications of this technology.

ICML

The first keynote address at the 41st International Conference on Machine Learning (ICML) 2024 in Vienna covered the changing nature of the machine learning field: from collaborative to increasingly competitive, it is becoming more exclusive and expensive due to the resources and compute power needed for this research.

AI was mostly applied in university and industrial lab research until 2022. However, due to its quick user uptake, the primarily academic subject became extremely profitable.

AI technology was primarily used for research projects at university and corporate labs before to 2022. ChatGPT was primarily intended to be a test when OpenAI made it available to the public in late 2022, but its quick user uptake transformed the primarily academic sector into a goldmine.

Speaking to 500 people in one of the largest rooms at the venue, Soumith Chintala, who oversees AI projects at Meta in New York, stated, “GPU compute and pretraining of data needs the deep pockets of billionaires. VCs and large corporates.”

Companies have been ceasing open research more frequently as computing and engineering become more expensive and concerns about data safety, legality, and societal impact arise. Big firms care about data safety, legality, and societal impact, he continued, even though they are racing to be leaders in this research.

Twenty years ago, Katherine Heller, a research scientist in Responsible AI at Google Research and the program chair of ICML 2024, said, “they started off as a close-knit worldwide community of about 500 researchers.” Heller has been attending this  AI conference on a regular basis for the past twenty years.

ICML 2024 Results

She has been selecting seminars and tutorials for the week-long event, inviting speakers, scheduling events, and vetting entries for the previous few months. “This year, they evaluated over 10,000 papers from 12,000 authors, and they chose roughly 2,600 to present,” she stated, characterizing the ML community’s expansion as “exponential.”

At ICML 2024, the International Conference on Machine Learning, scientists must choose a choice. The community is divided between those who are eager to push machine learning’s boundaries and those who believe it is crucial to take the social implications of this powerful technology into account.

Extending the Boundaries of Innovations and Advancements in Machine Learning: This team of academics aims to improve the technical aspects of machine learning. They are passionate in improved model structures and algorithms as well as cutting-edge applications in fields like finance, healthcare, and autonomous systems. Through their efforts, unprecedented degrees of automation, pattern recognition, and predictive analytics are becoming possible.

ICML Instruction 2024

Developing more effective and efficient neural networks is one way to improve deep learning.

Looking into how machine learning could be changed by quantum computing.

Improving the explainability and interpretability of complex models to make them more understandable.

Scalability is the capacity to create algorithms that can handle big datasets with ease.

The process of combining edge computing, IoT, and machine learning with other state-of-the-art technologies is known as  AI integration.

Thinking About the Impacts on Society

Ethics and Responsibility

Still, many scientists emphasize how important it is to consider the broader implications of machine learning. They argue that ethical concerns, biases in AI systems, and the potential for abuse must be addressed in addition to scientific advancements. These experts call for a more controlled and cautious approach to ensure that machine learning benefits society as a whole.

ICML Workshops 2024

Ensuring that algorithms do not perpetuate or reinforce societal biases is a matter of fairness and bias.

Privacy: Ensuring user data security and maintaining confidentiality in machine learning applications.

Machine learning transparency: Explaining decisions and procedures to non-experts.

Regulation and governance involve creating ethical AI rules.

Social Impact: Examining how machine learning affects employment, equity, and social dynamics.

Crossing the Divide

Many attendees of ICML 2024 are aware of how important it is to balance these points of view. They argue that improving machine learning and thinking about how it affects society are complementing goals rather than antagonistic ones. Multidisciplinary research, broad education on the sociological aspects of machine learning, and cooperative efforts between engineers and ethicists are thought to be crucial next stages.

Suggestions and Discussions

Panels & Workshops: Dedicated talks about the implications of machine learning for society, law, and morality.

Collaborative research refers to studies including social scientists, engineers, and policymakers.

Public Engagement: Attempting to educate the broader population about the benefits and risks associated with machine learning.

To sum up

ICML 2024 highlights a pivotal point in the field of machine learning. An increasing awareness that, even as they push the boundaries of technological capabilities, they also need to make sure that these innovations are consistent with moral standards and cultural norms is evident in the ongoing disputes and conversations. This comprehensive approach ought to contribute to the development of a future where machine learning is both responsible and creative.

Read more on govindhtech.com

[kroolo-12]

Welcome to Kroolo, your smart productivity powerhouse!

In this tutorial, we'll guide you through effortlessly creating projects using templates, optimizing your workflow and time management.⏳

Navigate to Projects Section: Head to the project section and select "Create a Project" on the right side.

Choose Template Option: Select "Use a Template" from the options provided.

Explore Template Library: With over 100 templates available, find one tailored to your needs, whether for sales, marketing, engineering, or any other field.

Example: Standup Meeting Template: Let's explore using a standup meeting template. After selecting it, receive a brief overview and proceed if it suits your needs.

Seamless Project Setup: The template loads all task groups, organized by status, making your project setup seamless.

Customize Your Project: Further customize by filtering desired groups or adjusting specific aspects like priority, due date, or assignees. Add custom columns based on your project's requirements.

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[smhive]

AI Unleashed: Top ChatGPT Prompts for Success! 💻💡

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[kalyanikakarla12]

Take-off Edu Group an exciting platform for engineering projects to explore and engage with cuttingedge technologies like Artificial Intelligence, Machine Learning, Internet of Things, Robotics, and Biomedical Engineering. The projects mentioned in this article are just a glimpse of the incredible possibilities awaiting students and enthusiasts. By participating in these projects, individuals can enhance their knowledge, develop practical skills, and contribute to the advancement of these fields. So, embark on this journey with Take-off Edu Group and let your engineering projects take flight as you delve into the world of AI, ML, IoT, Robotics, and Biomedical Engineering!

[fashioninquisitor]

AI PROJECT BY CREATIVE DIRECTOR JASON DUZANSKY

[blckbasiltech]

Enhancing Team Efficiency with AI Project Management for Smarter Decisions

AI project management enhances team efficiency by delivering smarter decision-making tools that keep everyone aligned. From creating adaptive schedules to offering real-time updates, AI ensures teams have access to accurate and actionable data. It eliminates repetitive tasks, allowing team members to focus on critical objectives. By identifying potential bottlenecks and suggesting data-backed solutions, AI helps managers make well-informed decisions that drive productivity. Whether you’re dealing with tight deadlines or complex projects, AI equips your team with the tools to succeed effortlessly. For more info: https://blackbasiltech.com

[t00l-xyz-ai-news]

I'm a GitHub Expert and I'm Shocked by These Trending AI Projects! #93

I'm a GitHub Expert and I'm Shocked by These Trending AI Projects! #093 #githubprojects #aiprojects #autogpttutorials 👉 Try FREE Aiarty Image Enhancer: Enhance & Upscale images up to 32K : https://www.aiarty.com/midjourney-prompts/?ttref=2410-ytb-aia-aigcmj-mgg-text-3l "🚀Explore the latest trending open-source GitHub projects focusing on AI frameworks, large language models (LLMs), and AI-powered investment research tools. Whether you're a developer looking for new AI frameworks, an investor seeking advanced research tools, or an AI enthusiast exploring LLM innovations, this video has you covered! Join us as we delve into groundbreaking projects like RouteLLM for efficient LLM routing, OpenBB for AI-driven investment research, and much more. 👍 Like, share, and subscribe for more updates on AI advancements. Drop your thoughts and questions in the comments below! #AITechnology #GitHubProjects #Innovation #AutoGPTTutorial #tensorart" 📌 Video Project Details. 🔗 Get FREE AI Project Updates : https://manuagi.beehiiv.com/subscribe 📌 Important AI Tools (affiliate) 🔗 Build Your AI Startup : https://shipfa.st/?via=autogpt 🔗 AI Voice - https://try.elevenlabs.io/0wgaz29csuo5 🔗 Try NordVPN : https://nordvpn.sjv.io/autogpt 🔗 NextJS Directory : https://nextjsdirectory.com?aff=j1Dej 📌 Timestamps : 00:00 - Intro Part 00:28 - o1-engineer : https://github.com/Doriandarko/o1-engineer 02:27 - BaseAI : https://github.com/LangbaseInc/BaseAI 04:39 - Crawl4AI : https://github.com/unclecode/crawl4ai 07:01 - ChatMLX : https://github.com/maiqingqiang/ChatMLX 09:23 - RouteLLM : https://github.com/lm-sys/RouteLLM 11:39 - OpenBB : https://github.com/OpenBB-finance/OpenBB 13:54 - EXO : https://github.com/exo-explore/exo 15:56 - Netdata : https://github.com/netdata/netdata 18:10 - Fragments by E2B : https://github.com/e2b-dev/fragments 20:25 - GenAI Agents: https://github.com/NirDiamant/GenAI_Agents 👍 Enjoyed the breakdown? Give us a thumbs up! 🔔 Stay updated with the latest in tech by subscribing. 💬 Share your thoughts or suggest projects for our next review in the comments!" Tags: I'm a GitHub Expert and I'm Shocked by These Trending AI Projects,GitHub Expert,Trending AI Projects,Github projects,github tutorial,github,ai projects,ai,ai tools,open source projects,open source,ai news,autogpt,manuagi,elon musk,chatgpt,RouteLLM,OpenBB,BaseAI,PodSnap AI,GenAI Agents 📈 Subscribe for more AI tutorials, tips, and industry insights. Don't forget to like, comment, and share with your tech-savvy friends! Hashtags: #OpenSourceProjects #GitHubTrends #CodingWonders #AIDevelopment #MachineLearning #ArtificialIntelligence #TechInnovation #MLflow #Playwright #OpenSource #AIProjects #DevTools #LeapAI #TechnologyTrends #AIInnovation #LLM #AIProjects #DeepLearning #MachineLearning #TechTrends #FutureOfAI #mentat #TechInnovation #FutureOfAI #AIProjects #DeveloperTools #AutoGPTTutorials #AITech #GitHubProjects #Innovations #TechTrends source Read the full article

[mirrorworldmagazine]

The concept of an AI project promises a higher yield for agribusiness

Farmers may be able to make more informed decisions and earn more money with the help of a novel, AI-driven method for crop planning and purchasing agricultural inputs.

Based on deep learning and game theory, GRAMA (Game Theory, Random Processes, Artificial Intelligence, and Machine Learning for Agriculture) is a NABARD-backed project.

Experts from the University of Agricultural Sciences, GKVK, are involved in the project, which is directed by Y Narahari, an honorary professor at the IISc’s Department of Computer Science and Automation.

Through crop planning, crop recommendation, procurement processes, market mechanisms, and price prediction, GRAMA seeks to maximize farmers’ earnings.

The initiative makes use of the data already available on soil types, crop varieties specific to a district, demand, yield, past prices, seasonal fluctuations, and sociocultural influences. If farmers used the proper changes in crop combinations, researchers predicted that their income would increase by 60%.

It indicates that we aren’t even correctly planning the crops. Prof. Narahari told DH that people who have farmed a certain crop variety for a long period might not want to switch to a new crop because they think they already have the necessary knowledge and are resistant to change.

A feature of the model allows one to ascertain how changes in crop combinations affect revenue. “The revenue can increase by 10% even with a 50% compliance rate,” he said.

Enhancing Farmer Producer Organizations (FPOs) is deemed essential by the researchers for expanding the study. They started experimental projects at FPOs in Rajaghatta and Anekal earlier this year, and the FPOs were very enthusiastic, even if the farmers needed to be persuaded about the technology. Prof. Narahari thinks that GRAMA could eventually become an essential part of farming methods, nevertheless, if progressive farmers get more involved and the government issues a mandate.

Bharat Electronics Limited (BEL) developed a mobile app that was used at the two FPOs to evaluate a pilot version of the procurement system. The farmers’ cooperatives gather the necessary information from the farmers. Based on these requirements and current information on several parameters including weather and soil type, the app advises aggregate amounts of seeds, fertilizers, and pesticides to be acquired. The concept suggests a framework that ensures farmer procurement at a reasonable price with consistent quality.

According to Prof. Narahari, the implementation of GRAMA required support from initiatives to reduce transportation and maximize the utilization of available warehouses and other resources. He also mentioned several real-world difficulties.

According to Prakash Kammaradi, an agricultural economist, future farming innovation must offer more specific and financially rewarding price discovery, produce aggregation, and efficient distribution mechanisms.

“Agrarian problems are very complex and challenging. In addition to field experience, the application of cutting-edge technology can give us a deep understanding of these problems. However, solely relying on it for solutions might not be feasible in the long term,” he added.

[projectmanagertemplate]

How Artificial Intelligence is Transforming Project Management

Artificial Intelligence (AI) is no longer a futuristic concept confined to science fiction; it's a powerful tool reshaping various industries, including project management. From enhancing productivity to predicting project outcomes, AI brings a multitude of benefits that can revolutionize how projects are planned, executed, and monitored. Here's a look at how AI can be leveraged in project management to drive efficiency and success.

[shuttech]

TikTok Owner Fires Intern Over Alleged AI Project Sabotage

TikTok's parent company has fired an intern following accusations of sabotaging a crucial AI project. The alleged internal disruption has sparked concerns over security and ethics within the tech giant. With the rapid development of AI technologies, this incident raises questions about trust and responsibility in corporate innovation.

https://shuttech.com/technology/tiktok-owner-fires-intern-over-alleged-ai-project-sabotage/

[govindhtech]

Lean AI Is Making Modern Technology Access To All Business

 AI is changing thanks to open source and small language models. Lean AI is changing the business environment and democratizing cutting-edge technology for companies of all sizes by reducing costs and increasing productivity.

AI Lean

The term “lean” is frequently used in the IT business to refer to procedures that need to be more economical and efficient. This also applies to generative AI. In case you missed it, certain businesses demand gigawatts of grid power in addition to equipment that cost millions of dollars to operate. It seems sense that a lot of businesses approach AI architects to offer a leaner or more effective solution.

Businesses naturally turn to public cloud providers to accelerate their adoption of generative AI. Ultimately, public clouds provide entire ecosystems with a single dashboard button click. In fact, this initial wave of AI spending has increased income for major cloud providers.

Many businesses have discovered, nevertheless, that employing the cloud in their data centers might result in greater operational expenses than traditional systems. Companies are looking at ways to use cloud costs more efficiently because, in spite of this, using the cloud is still the major focus. This is where the idea of “lean AI” is useful.

How is lean AI implemented?

A strategic approach to artificial intelligence known as “lean AI” places an emphasis on productivity, economy, and resource efficiency while generating the most economic value possible. Lean approaches that were first applied in manufacturing and product development are the source of many lean AI techniques.

The goal of lean AI is to optimize AI system development, deployment, and operation. To cut down on waste, it uses smaller models, iterative development methods, and resource-saving strategies. Lean AI emphasizes continuous improvement and agile, data-driven decision-making to help firms scale and sustain AI. This ensures AI projects are impactful and profitable.

SLMs

Businesses are starting to realize that sometimes, bigger does not always mean better. A wave of open source advances and small language models (SLMs) characterize the evolving industrial AI ecosystem. Large language model (LLM) generative AI systems impose significant costs and resource demands, which have prompted its evolution. These days, a lot of businesses wish to reevaluate how expenses and business value are distributed.

The difficulties associated with LLMs

Big language models, like Meta’s Llama and OpenAI’s GPT-4, have shown remarkable powers in producing and comprehending human language. However, these advantages come with a number of difficulties that businesses are finding harder and harder to justify. The hefty cloud expenses and computational needs of these models impose a burden on budgets and prevent wider implementation. Then there is the problem of energy use, which has serious financial and environmental ramifications.

Another challenge is operational latency, which is particularly problematic for applications that need to respond quickly. Not to mention how difficult it is to manage and maintain these massive models, which call for infrastructure and specialized knowledge that not all organizations have access to.

What Are SLMs

Making the switch to SLMs

The deployment of tiny language models for generative AI in cloud and non-cloud systems has been expedited by this background. These are becoming more and more thought of as sensible substitutes. In terms of energy usage and the need for computational resources, SLMs are made to be substantially more efficient.

This translates into reduced operating expenses and an alluring return on investment for AI projects. Enterprises that require agility and responsiveness in a rapidly evolving market find SLMs more attractive due to their accelerated training and deployment cycles.

It is absurd to imply that enterprises will employ LLMs as they are not typically used by them. Rather, they will develop more tactically focused  AI systems to address particular use cases, such factory optimization, logistics for transportation, and equipment maintenance areas where lean AI approaches can yield instant commercial value.

SLMs refine customisation as well. By fine-tuning these models for certain tasks and industry sectors, specialised apps that deliver quantifiable business outcomes can be produced. These slimmer models demonstrate their efficacy in customer support, financial analysis, and healthcare diagnosis.

The benefit of open source

The development and uptake of SLMs have been propelled by the open source community. Llama 3.1, the latest version of Meta, comes in a variety of sizes that provide strong functionality without putting too much strain on system resources. Other models show that the performance of smaller models can match or even exceed that of bigger models, particularly in domain-specific applications. Examples of these models are Stanford’s Alpaca and Stability AI’s StableLM.

Hugging Face, IBM’s Watsonx.ai, and other cloud platforms and tools are lowering entry barriers and increasing the accessibility of these models for businesses of all sizes. This is revolutionary AI capabilities are now accessible to everyone. Advanced AI can be included by more companies without requiring them to use proprietary, sometimes unaffordable technologies.

The business turn around

There are several benefits to using SLMs from an enterprise standpoint. These models enable companies to grow their AI implementations at a reasonable cost, which is crucial for startups and midsize firms looking to get the most out of their technological expenditures. As AI capabilities are more closely aligned with changing business needs through faster deployment timeframes and simpler customization, enhanced agility becomes a practical benefit.

SLMs hosted on-premises or in private clouds provide a superior solution for addressing data privacy and sovereignty, which are recurring problems in the enterprise environment. This method maintains strong security while meeting regulatory and compliance requirements. Furthermore, SLMs’ lower energy usage contributes to business sustainability programs. Isn’t that still significant?

The shift to more compact language models, supported by innovation in open source, changes how businesses approach artificial intelligence. SLMs provide an effective, affordable, and adaptable alternative to large-scale generative AI systems by reducing their cost and complexity. This change promotes scalable and sustainable growth and increases the business value of AI investments.

Read more on govindhtech.com

[mohsinazadai]

Prompt

A courageous mouse, armed with a magical sword, ventures into a dark, cursed forest to free it from the grip of an evil sorcerer. Along the way, the mouse must rally the forest’s inhabitants—enchanted trees, talking animals, and mystical beings—to unite in a battle against the sorcerer’s minions and break the curse.

#ArtAndTechnology #AIImagination #ArtWithAlgorithms #VirtualCreativity #NextGenArt #AIArtisticVision #GenerativeArt #TechArtistry #MohsinAzadai #FutureOfArt #SmartHome #AILifestyle #NeuralNetworks #ComputerVision #NaturalLanguageProcessing #AIResearch #AIDevelopment #AISolutions #AIProjects #AITrends #NextGenTechnology #InnovativeTechnology #InstaAI #AICommunity #AIArtists #TechArtists #DigitalCreators #CreativeCommunity #PinterestAI #AIPins #AIOnPinterest