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Learning in AI can fall under the types “narrow intelligence,” “artificial general intelligence,” and “super.” These categories demonstrate AI’s capabilities as it evolves—performing narrowly defined sets of tasks, simulating thought processes in the human mind, and performing beyond human capability.

The idea of boosting building new AI computer vision applications with a different way of implementation through involving the human in the loop. Know more from the article and my session tomorrow with AI Cafe

Blog Post: Understanding Artificial Intelligence In the ever-changing world of engineering, a key player has arrived: Artificial Intelligence. With AI changing nearly every area in engineering, the need for more accurate AI models is becoming increasingly necessary. However, the challenge faced when using AI, is whether the results received, and decisions made by these complex AI models can be trusted. This is where explainable AI comes in. Explainable AI is a set of methods and processes which allows engineers to understand and trust the results from machine learning algorithms. Put simply: knowing how the results were achieved and understanding how the AI model makes decisions allows us to verify and trust the results.  Read more - https://lnkd.in/d9xCTiBd #artificialintelligence #explainableai

Artificial Intelligence (AI) and Machine Learning (ML) have transformed the art of application development and made it smarter, more efficient, and user-centric. AI and ML has revolutionized the industries associated with application development – thus, helping so many other industries to get to the next level. 

If you've been following the latest developments in AI, you've probably heard about Retrieval-Augmented Generation (RAG). It's a game-changer that's enhancing the accuracy and reliability of generative AI models by incorporating facts from external sources, like your own documents. Traditional Language Model (LLM) neural networks are fantastic at responding to general prompts, but they can stumble when users need company specific or up-to-date information. That's where RAG comes in. Companies can convert their product documentation, manuals, videos, or logs into knowledge bases to enhance LLMs. These supercharged AI models can then be deployed for customer support, employee training, and team productivity. It's no wonder that major companies like AWS, IBM, Google, Microsoft, NVIDIA, Oracle, and Pinecone are jumping on the RAG bandwagon. At Lighthouse Organizational Development, we're harnessing the power of RAG to bring AI to teams and train custom assistants on team and company knowledge. We're excited about the potential of this technology to revolutionize how teams work and communicate. Are you using RAG-enhanced AI assistants in your work? #AI #ArtificialIntelligence #TeamEffectiveness #RAG #FutureOfWork #Innovation #Productivity

Professor Yann LeCun recently gave a talk titled "From Machine Learning to Autonomous Intelligence" The presentation covers an important topic: How could machines learn, remember, reason, plan, and have common sense, yet be steerable and safe? Here is a summary: 1. "AI will affect every aspect of human activity" There are numerous benefits of AI in many different areas of science and everyday life. From medicine, transportation to arts and education. 2. "Self-Supervised Learning has taken over the world" Models like BERT and other foundation models have changed the way we use machine learning. They can be used for "understanding and generating text, images, video, 3D models, speech, proteins, and more." 3. Auto-Regressive Large Language Models suck Performance is amazing but - They make stupid mistakes - They have limited knowledge of the underlying reality - They have no common sense - They can’t plan their answer 4. Current AI Technology is (still) far from Human Level "There is no questions that, eventually, machines will eventually surpass human intelligence in all domains" 5. What's more in the talk: - Three challenges for AI & Machine Learning - Objective-Driven AI - How could machines learn to understand the world? - Energy-Based Models - What is AGI? How long is it going to take to reach human-level AI? - Will robots take over the world? All the information is taken from the talk recording and slides. Video: https://lnkd.in/evj6nxtb Slides: https://lnkd.in/eXpAXHMf

I once had a conversation with a Director of an AI Company. During the conversation, I asked a few basic AI algorithm questions, and surprisingly, he was clueless! 😂 The whole company has "AI" in its name but lacks fundamental knowledge. For those starting out: What is AI? Full-Form: Artificial Intelligence Simple Definition: The algorithmic process of data, combining, altering, and presenting. Key Metrics to enable AI: We need CUDA processing to run complex AI, mainly provided by Nvidia at the moment.(GPU Heavy) Another way: Using Machine Learning with basic automation can mimic AI, but it won't be pure AI, just a less smart model. In conclusion, understanding the core concepts is crucial in the rapidly evolving field of Artificial Intelligence. It's not just about the name; it's about the knowledge that drives innovation. 90% or more companies, Don't actually use proper technique or practices to run the model, all namesake marketing gimmick. #AI #ArtificialIntelligence #Revault #MachineLearning #AIatitsPeak #OpenAI #Data #algorithms #DSA

Machine learning and AI are shaving (many) years off the timeline from hypothesis to result at our Silicon Valley lab. Here’s how: #NissanUSA #AI #Data #MachineLearning #Auto #Tech #Innovation

What is the purpose of machine learning? Interesting as its title might be, machine learning is the subset of artificial intelligence (AI) that does the least learning. Nonetheless, it is one of the most ubiquitous types of AI. Machine learning is a type of software that aims to automate and simplify processes with simple programs. It is ever present in the modern world, being used in nearly all industries. Automatic responses to queries, automated stock trading, computer vision, recommendation engines, and customer service are some examples of the utilities of machine learning. Machine learning is a large market, encompassing the majority of AI software and projects. In line with this, the machine learning market is also the largest segment of the AI market. This market is expected to grow from around 140 billion U.S. dollars to nearly two trillion U.S. dollars by 2030. #machinelearning #ai #aiautomation

Is Prompt Engineering Dead? 💡🤔 In the world of AI and machine learning, the discussion around prompt engineering has been both intriguing and controversial. Some argue that it’s an outdated approach, while others believe it still holds immense value in shaping AI models. But is it really dead? Prompt engineering has been a cornerstone in fine-tuning AI models, enabling us to guide these systems by providing specific instructions or examples. It's been instrumental in achieving remarkable results across various applications. However, the landscape is evolving rapidly. New advancements like self-supervised learning and more powerful models are challenging the traditional notions of prompt engineering. These innovations prompt us to reconsider its role and potential limitations. At its core, prompt engineering embodies the human-AI interaction, bridging the gap between our intentions and the model's outputs. Even as we explore new avenues, the essence of understanding how to communicate effectively with AI remains pivotal. So, is prompt engineering obsolete, or does it still have a place in shaping the future of AI? I believe it’s a discussion worth delving into. Let's keep the conversation alive! Follow Vivek Singh Kalakoti for more such Amazing Content Video Credits: Shawn kanungo #AI #MachineLearning #PromptEngineering #FutureTech #TechDebates