Mega tech trends like the cloud, the mobile phone era, metaverse and now AI all depend on enabling technologies sitting right beneath the surface hidden from nearly everyone’s view. Their structural integrity depends on the flawless operation of those enabling technologies, which in many cases are Application Programming Interfaces (APIs). As such, their success depends on API adoption. Nowhere is this truer than in the rapid proliferation of AI technologies, like generative AI, which require a simple and very easy-to-use interface that gives everyone access to the technology. The secret here is that these AI tools are just thin UIs on top of APIs that connect into the highly complex and intensive work of a large language model (LLM).
It’s important to remember that AI models don’t think for themselves, they only appear to be so that we can interact with them in a familiar way. APIs are essentially acting as translators for AI platforms as they’re relatively straightforward, highly structured and standardized on a technological level. What most people think of as “AI” should be viewed through the lens of an API product; and with that mindset, organizations can best prepare for what potential use cases are possible and how to ensure their workforces have the skills to put them into action. — Read More
Tag Archives: DevOps
Machine Learning Libraries For Any Project
There are many libraries out there that can be used in machine learning projects. Of course, some of them gained considerable reputations through the years. Such libraries are the straight-away picks for anyone starting a new project which utilizes machine learning algorithms. However, choosing the correct set (or stack) may be quite challenging.
In this post, I would like to give you a general overview of the machine learning libraries landscape and share some of my thoughts about working with them. — Read More
Qwen-VL: A Frontier Large Vision-Language Model with Versatile Abilities
We [Alibaba] introduce the Qwen-VL series, a set of large-scale vision-language models designed to perceive and understand both text and images. Comprising Qwen-VL and Qwen-VL-Chat, these models exhibit remarkable performance in tasks like image captioning, question answering, visual localization, and flexible interaction. The evaluation covers a wide range of tasks including zero-shot captioning, visual or document visual question answering, and grounding. We demonstrate the Qwen-VL outperforms existing Large Vision Language Models (LVLMs). We present their architecture, training, capabilities, and performance, highlighting their contributions to advancing multimodal artificial intelligence. Code, demo and models are available at https://github.com/QwenLM/Qwen-VL. — Read More
AIColor: Colorize your old Photos with the power of AI
If you’re looking to colorize old black and white photos, our AI photo colorizer can help you bring your memories to life. — Read More
Introducing Code Llama, a state-of-the-art large language model for coding
Today, we are releasing Code Llama, a large language model (LLM) that can use text prompts to generate code. Code Llama is state-of-the-art for publicly available LLMs on code tasks, and has the potential to make workflows faster and more efficient for current developers and lower the barrier to entry for people who are learning to code. Code Llama has the potential to be used as a productivity and educational tool to help programmers write more robust, well-documented software. — Read More
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LLMStack
LLMStack is a no-code platform for building generative AI applications, chatbots, agents and connecting them to your data and business processes.
Build tailor-made generative AI applications, chatbots and agents that cater to your unique needs by chaining multiple LLMs. Seamlessly integrate your own data and GPT-powered models without any coding experience using LLMStack’s no-code builder. Trigger your AI chains from Slack or Discord. Deploy to the cloud or on-premise. — Read More
AI2 drops biggest open dataset yet for training language models
Language models like GPT-4 and Claude are powerful and useful, but the data on which they are trained is a closely guarded secret. The Allen Institute for AI (AI2) aims to reverse this trend with a new, huge text dataset that’s free to use and open to inspection.
Dolma, as the dataset is called, is intended to be the basis for the research group’s planned open language model, or OLMo (Dolma is short for “Data to feed OLMo’s Appetite). As the model is intended to be free to use and modify by the AI research community, so too (argue AI2 researchers) should be the dataset they use to create it. — Read More
Meta’s Next Big Open Source AI Dump Will Reportedly Be a Code-Generating Bot
Meta’s language-centric LlaMA AI will soon find itself in the company of a nerdier, coding wiz brother. The company’s next AI release will reportedly be a big coding machine meant to compete against the proprietary software from the likes of OpenAI and Google. The model could see a release as soon as next week.
According to The Information who spoke to two anonymous sources with direct knowledge of the AI, this new model dubbed “Code Llama” will be open source and available free online. This is consistent with the company’s strategy so far of releasing widely available AI software that makes developing new customizable AI models much easier for companies who don’t want to pay OpenAI or others for the privilege. — Read More
Doctor GPT
DoctorGPT is a Large Language Model that can pass the US Medical Licensing Exam. This is an open-source project with a mission to provide everyone their own private doctor. DoctorGPT is a version of Meta’s Llama2 7 billion parameter Large Language Model that was fine-tuned on a Medical Dialogue Dataset, then further improved using Reinforcement Learning & Constitutional AI. Since the model is only 3 Gigabytes in size, it fits on any local device, so there is no need to pay an API to use it. It’s free, made for offline usage which preserves patient confidentiality, and it’s available on iOS, Android, and Web. Pull requests for feature additions and improvements are encouraged. — Read More
IBM and NASA teamed up to build the GPT of Earth sciences
NASA estimates that its Earth science missions will generate around a quarter million terabytes of data in 2024 alone. In order for climate scientists and the research community efficiently dig through these reams of raw satellite data, IBM, HuggingFace and NASA have collaborated to build an open-source geospatial foundation model that will serve as the basis for a new class of climate and Earth science AIs that can track deforestation, predict crop yields and rack greenhouse gas emissions. — Read More
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