Patrick M. on LinkedIn: Here are the latest updates in the world of AI: 1. AI is revolutionizing… (2024)

Yes, AI can do many things, but NOT everything, at least NOT making love, nor what we are doing, by using our intellectual property (IP), a copyrighted multilingual metadata, in data analytics.Metadata is an enabler. Without metadata, NO data can be found/retrieved, even by the most advanced technologies, like AI, NVIDIA chips, supercomputers, etc.https://lnkd.in/g-aJFnXRExperiment results showed that, with our intellectual property (IP), a copyrighted multilingual metadata, we are doing what AI, like ChatGPT, can't do in data analytics.Our IP can also make your information service UNIQUE in the world.Do you or any of your contacts need our expertise and our IP? We're selling, NOT just talking. " Thanks.P.S. we wrote this post manually, without using AI.

The era ofgenerative AIis here, ushered in by neural networks capable of generating new data, creating content and making informed decisions based on extensive training datasets.But exactly what generative AI looks like is increasingly an open question: WhileChatGPT ushered in a waveof similar chatbot-style, text-based interfaces, others including Hume AI and even OpenAI competitor Anthropic have taken alternative approaches.Meanwhile, Google and Microsoft have augmented their search engines with gen AI and new gen AI content creation models such as Stable Diffusion, Midjourney, Runway’s Gen-3 Alphas and Luma AI’s Dream Machine have all offered even more interesting web-based interfaces and apps with dials, buttons, slider bars and other methods of controlling or influencing AI models to generate more specific outputs.

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Introduction:How a child starts learning to recognize the things? The answer is the cognition through eyes and the perception through ears. If a term called Pencil is fixed into its senses, it will never fall short to recognize Pencil again and again. Similarly, machines can be trained to recognize and perceive things properly. It's not a Sci-Fiction story it's damn good reality.Definition of Machine Learning:By definition, Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.Machines are getting intelligent day-by-day. If you want step outside your home, you want to hire a cab service, you use app, that gives you optimized way of reaching destination avoiding traffic and saving your money. The optimization comes from Machine Learning (ML). The Alexa from Amazon, Siri from Apple, Google Assistant helps you to work around your daily schedules. These automatic responding speech machines are nothing but ML powered ones. The chat-bot Sophia is one of them. The surveillance cameras on the road detecting the violence of traffic rules become autonomous by ML. The online shopping suggestions when you open Amazon app, Facebook, Google are provided to us by ML with your previous shopping experiences.How did these Tech Giants achieve this? Simple answer is they collected data by providing you the Free app and working on that data, they made your experience with shopping, searching easy. So, whenever you get free of cost, make sure that you are the target for information. The FaceApp gives you the old age, young age photos on button click is ML in Image processing. The stock market predictions, or a business growth predictions can be done with ML to the perfection.Nowadays the doctors have started getting help from Robots for surgery, it’s a AI part. General Motors is designing a driver-less car with the help of AI. The humans can not go in the places, Robots are fighting at that spot with fire for rescue and survival. This is again AI. The automated spacecrafts are now reaching Mars, Moon, this is the big achievement of AI. The machines have started playing with us. IBM’s deep blue computer has 6 matches with Garry Kasparov in 1996.If you think, with the help of AI, your fiction becomes reality.

The race to make the best generative AI is heating up fast. The AI world barely digested OpenAI's newest announcement of GPT4-o, when Google unleashed a flood of new stuff today,- Improved Gemini, Veo, Project Astra, and more. Now Apple hints at big AI news coming soon.Ever since ChatGPT came out, it feels like tech companies are launching new AI language models almost every day. Each big company tries to outdo the others with bigger claims and tiny improvements. But in the middle of all these fast releases, I can't help but wonder: Where's the real use for regular people?From what I can see, the big rush to make generative AI seems driven more by publicity battles than truly helpful innovations. Every week brings flashy unveilings and grand promises. What we're seeing is an "industry rushing ahead before it can walk." The frantic model releases appear aimed at creating buzz, battling rivals, and keeping investors happy as they race to become number one in AI. But is providing sustained value to users getting left behind in this AI arms race?Recently, I came across eye-opening stories from engineers under intense pressure to feed this hype cycle. They describe burnout from rushed, sloppy releases that often get canceled. There are reports of cutting corners on ethics, security flaws, and releases tainted by bias - all sacrificed for the sake of speed and branding.Meanwhile, game-changing applications that use AI's potential in ethical, accessible ways for everyone remain on the horizon. The true "revolutionary moment" we were promised feels distant with each new over-hyped launch.So to all the AI companies: by all means, keep raising the bar. But don't forget to take a pause and show the true disruption we're waiting for.#GenerativeAI #ArtificialIntelligence #Innovation #TechReview #UserExperience

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🤖 Missed this week's AI News?Don't worry! We always keep you up to date! 😀Let's explore this week's Magic AI News together.👇🏽1️⃣ GPT-4o: OpenAI combines audio, text, and vision in one model- GPT-4o is available for all users in ChatGPT (free and paid)- Lower latency and more cost-efficient because of the end-to-end approach (all-in-one model)- It is possible to interrupt ChatGPT. More realistic conversations are possible.You can watch the presentation in the following video.https://lnkd.in/enS35VAT2️⃣ Google I/O 2024 with many AI updatesSome updates in a brief overview:- Introducing Gemini 1.5 Flash with a 1 million token context window for high-frequency tasks- Launch of the open-source model Gemma 2 with 27B parameters in June- New open vision-language model called PaliGemma- New video generation model Veo, a competitor to OpenAI's Sorahttps://lnkd.in/em-VPErt3️⃣ Humanoid robot for 16,000 dollars?Imagine having a humanoid robot for just $16,000. Unitree Robotics has unveiled the Unitree G1, an affordable all-purpose robot with state-of-the-art sensors and AI.https://lnkd.in/eXjS_fQN🎬 Want to know more about these exciting developments in AI and the tech world?You can find our thoughts on the news in the full newsletter! 👇🏽 #artificialintelligence https://lnkd.in/eT8S5yp2

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Google has unveiled three new "open" generative AI models, claiming they are "safer," "smaller," and "more transparent" compared to others. These models, part of Google's Gemma 2 family, aim to provide different functionalities while focusing on safety. The new models include:1.Gemma 2 2B: A lightweight model designed for generating and analyzing text, capable of running on various hardware, including laptops and edge devices. It is available for certain research and commercial uses and can be accessed through platforms like Google's Vertex AI model library, Kaggle, and Google’s AI Studio toolkit.2.ShieldGemma: This consists of "safety classifiers" that detect toxicity, such as hate speech, harassment, and explicit content. ShieldGemma is built on top of Gemma 2 and can filter both prompts to a generative model and the content produced by it.3.Gemma Scope: This tool allows developers to delve into specific aspects of a Gemma 2 model, making its processes more interpretable. Google describes Gemma Scope as specialized neural networks that help unpack the complex information processed by Gemma 2, making it easier to analyze and understand.The Gemma models are distinct from Google's Gemini models, which are not open source and are used by Google's products and available to developers. Gemma models aim to build goodwill within the developer community, similar to Meta’s approach with Llama.The release of these models aligns with a recent U.S. Commerce Department report endorsing open AI models, emphasizing their importance in making generative AI accessible to smaller companies, researchers, nonprofits, and individual developers #LLM #AI #GenerativeAI #GenAI

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Regarding AI, I have firmly believed, a powerful front end with the ability to interact with users in a seamless manner and does/assists in the task that matters is one who will win the race in the end. A very interesting read.

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The Morning After: The cost of generating AI images: Carnegie Mellon and Hugging Face researchers found that AI-generated images consume as much energy as charging a smartphone, while text AI tasks like chatbots use less. They encourage transparency in the environmental impact of AI models, noting the high energy use of popular AIs like ChatGPT. - Artificial Intelligence topics! #ai #artificialintelligence #intelligenzaartificiale

🤖 Missed this week's AI News?Don't worry! We always keep you up to date! 😀Let's explore this week's Magic AI News together. 👇🏽1️⃣ Apple unveils OpenELM for on-device AI- Family of eight models (four models are instruction-tuned) with four different parameter sizes (270M, 450M, 1.1B, 3B)- Apple also open-sourced its library CoreNet. CoreNet is a library for training deep neural networks.2️⃣ Sanctuary AI presents Gen 7 of its humanoid robot Phoenix- The robot can learn new tasks in less than 24 hours- Longer operating times and significantly lower cost of manufacture3️⃣ Microsoft releases a new SLM for on-device AI- According to Microsoft "Phi-3 models are the most capable and cost-effective small language models (SLMs) available"- It is instruction-tuned and available on Microsoft Azure, Hugging Face and Ollama🎬 Want to know more about these exciting developments in the AI and tech world?You can find our thoughts on the news in the full newsletter! 👇🏽 #ArtificialIntelligence #AINewshttps://lnkd.in/e29d2Um8

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We don't know much about OpenAI's new project BUT rumors suggest it could be a game-changer for AI reasoning. Here's why this is important:Reasoning is the cornerstone of human intelligence. It’s what allows us to solve complex problems, make informed decisions, and navigate the world with nuance. For AI to truly augment human capabilities, it must excel in reasoning.Enter Project Strawberry🍓Leaked documents and recent teasers from Sam Altman suggest that Project Strawberry aims to bring AI reasoning to new heights.This model is designed to autonomously navigate the internet, perform deep research, and solve intricate problems with human-like precision.What does this mean for us?- Enhanced Decision-Making: AI that can reason will provide more accurate and contextually relevant insights.- Autonomous Research: Imagine AI conducting thorough research without constant human prompts.- Future of Work: This could revolutionize industries by automating complex tasks that require deep understanding and reasoning.But here’s the big question:How do you see advanced reasoning in AI transforming your industry? Let’s discuss! Share your thoughts, experiences, and predictions in the comments below. 👇

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