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“Experience X’s Latest Upgrade: Grok-1.5 for the Futuristic Chatbot, Grok”

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X.ai, Elon Musk’s AI startup, has revealed its latest generative AI model, Grok-1.5. Grok-1.5 benefits from “improved reasoning,” according to X.ai, particularly where it concerns coding and math-related tasks. One improvement that should lead to observable gains is the amount of context Grok-1.5 can take in compared to Grok-1. Context, or context window, refers to input data (in this case, text) that a model considers before generating output (more text). The announcement of Grok-1.5 comes after X.ai open sourced Grok-1, albeit without the code necessary to fine-tune or further train it.

New AI Developed by AI21 Labs Surpasses Competitors in Context-Based Task Comprehension

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Increasingly, the AI industry is moving toward generative AI models with longer contexts. Ori Goshen, the CEO of AI startup AI21 Labs, asserts that this doesn’t have to be the case — and his company is releasing a generative model to prove it. Contexts, or context windows, refer to input data (e.g. Trained on a mix of public and proprietary data, Jamba can write text in English, French, Spanish and Portuguese. Loads of freely available, downloadable generative AI models exist, from Databricks’ recently released DBRX to the aforementioned Llama 2.

“Databricks’ $10M Investment in DBRX Failed to Surpass GPT-4’s Dominance in AI Generation”

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You could spend it training a generative AI model. See Databricks’ DBRX, a new generative AI model announced today akin to OpenAI’s GPT series and Google’s Gemini. Customers can privately host DBRX using Databricks’ Model Serving offering, Rao suggested, or they can work with Databricks to deploy DBRX on the hardware of their choosing. It’s an easy way for customers to get started with the Databricks Mosaic AI generative AI tools. And plenty of generative AI models come closer to the commonly understood definition of open source than DBRX.

Generative AI Now Accessible to All Developers with Fireworks.ai Open Source API

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Just about everyone is trying to get a piece of the generative AI action these days. While lacking the brand name recognition of some of these other players, it boasts the largest open source model API with over 12,000 users, per the company. That kind of open source traction tends to attract investor attention, and the company has raised $25 million so far. “It can be either off the shelf, open source models or the models we tune or the models our customer can tune by themselves. Being an API, developers can plug it into their application, bring their model of choice trained on their data, and add generative AI capabilities like asking questions very quickly.

The Significance of Elon Musk’s Open-Source AI Company, Grok: A Critical Analysis

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Elon Musk’s xAI released its Grok large language model as “open source” over the weekend. But does releasing the code for something like Grok actually contribute to the AI development community? This isn’t the first time the terms “open” and “open source” have been questioned or abused in the AI world. So where does xAI’s Grok release fall on this spectrum? Is his nascent AI company really dedicated to open source development?

Grok: xAI Releases Base Model with No Training Code as Open Source

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Elon Musk’s xAI has open-sourced the base code of Grok AI model, but without any training code. In a blog post, xAI said that the model wasn’t tuned for any particular application such as using it for conversations. Last week, Musk noted on X that xAI intended to open-source the Grok model this week. Some AI-powered tool makers are already talking about using Grok in their solutions. Yep, thanks to @elonmusk and xAI team for open-sourcing the base model for Grok.

“Midjourney Takes on Copyright Police in Exciting AI Showdown”

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Generative AI models like Midjourney’s are trained on an enormous number of examples — e.g. Some vendors have taken a proactive approach, inking licensing agreements with content creators and establishing “opt-out” schemes for training data sets. The problem with benchmarks: Many, many AI vendors claim their models have the competition met or beat by some objective metric. Anthropic launches new models: AI startup Anthropic has launched a new family of models, Claude 3, that it claims rivals OpenAI’s GPT-4. AI models have been helpful in our understanding and prediction of molecular dynamics, conformation, and other aspects of the nanoscopic world that may otherwise take expensive, complex methods to test.

India abandons mandatory approval for AI model launches

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India is walking back on a recent AI advisory after receiving criticism from many local and global entrepreneurs and investors. The Ministry of Electronics and IT shared an updated AI advisory with industry stakeholders on Friday that no longer asked them to take the government approval before launching or deploying an AI model to users in the South Asian market. Under the revised guidelines, firms are instead advised to label under-tested and unreliable AI models to inform users of their potential fallibility or unreliability. The revision follows India’s IT ministry receiving severe criticism earlier this month from many high-profile individuals. Less than a year ago, the ministry had declined to regulate AI growth, identifying the sector as vital to India’s strategic interests.

Google’s Deepmind AI learns to be your ultimate video game ally

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AI models that play games go back decades, but they generally specialize in one game and always play to win. From this data — and the annotations provided by data labelers — the model learns to associate certain visual representations of actions, objects, and interactions. AI agents trained on multiple games performed better on games they hadn’t been exposed to. But of course many games involve specific and unique mechanics or terms that will stymie the best-prepared AI. And simple improvised actions or interactions are also being simulated and tracked by AI in some really interesting research into agents.

Aura: Deepgram’s Revolutionary AI Vocalizes Agents

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Aura combines highly realistic voice models with a low-latency API to allow developers to build real-time, conversational AI agents. Backed by large language models (LLMs), these agents can then stand in for customer service agents in call centers and other customer-facing situations. Deepgram’s Aura combines human-like voice models that render extremely fast (typically in well under half a second) and, as Stephenson noted repeatedly, does so at a low price. “Everybody now is like: ‘hey, we need real-time voice AI bots that can perceive what is being said and that can understand and generate a response — and then they can speak back,'” he said. The Aura model, just like all of the company’s other models, were trained in-house.