Explore AI milestones 📜

AI is rapidly developing and changing our lives. But how did we get to where we are today? Let’s take a look at the timeline of artificial intelligence development over the decades:

📆 1997: a machine beats a human at chess
The IBM’s Deep Blue supercomputer was only good at one thing: chess. This was a specialized system equipped with such a massive database of chess moves that it can’t be memorized by a human.

📆 2013: the start of the modern AI era
The authors of the paper Efficient Estimation of Word Representations in Vector Space had suggested a simple mathematical model for establishing connections between words and using them to train neural networks.

📆 2017: neural network wins a game of Dota 2 against the reigning world champions
What’s interesting about this case is the speed with which the AI was developed. The bot played its first game in May – and by August it was able to outpace the planet’s strongest players.

📆 2020: the emergence of GPT
This language model amplified the efficiency of neural networks through increased computer capacity. It also produced the world’s first Copilot network, which provides suggestions on software code with high accuracy.

📆 2023: the release of the NVIDIA H100 graphics card
The capacity of ten such cards combined is near to the capacity of the human brain. It was now possible to develop a supercomputer as strong as the human brain and yet no larger than an office desk.

Guest post by ITMO University

Over the weekend, the xAI team brought their Colossus 100k H100 training cluster online. From start to finish, it was done in 122 days.

Colossus is the most powerful AI training system in the world. Moreover, it will double in size to 200k when they add a further 50k H200s in a few months.

What does this mean?

Faster training
Bigger and more complex models
New features

Bigger is better, good times are coming.

GPT-4o mini: advancing cost-efficient intelligence

GPT-4o mini surpasses GPT-3.5 Turbo and other small models on academic benchmarks across both textual intelligence and multimodal reasoning, and supports the same range of languages as GPT-4o. It also demonstrates strong performance in function calling, which can enable developers to build applications that fetch data or take actions with external systems, and improved long-context performance compared to GPT-3.5 Turbo.

GPT-4o mini has been evaluated across several key benchmarks2.

Reasoning tasks: GPT-4o mini is better than other small models at reasoning tasks involving both text and vision, scoring 82.0% on MMLU, a textual intelligence and reasoning benchmark, as compared to 77.9% for Gemini Flash and 73.8% for Claude Haiku.

Chatgpt prompt

GPT-4o System Card

We thoroughly evaluate new models for potential risks and build in appropriate safeguards before deploying them in ChatGPT or the API. We’re publishing the model System Card together with the Preparedness Framework scorecard to provide an end-to-end safety assessment of GPT-4o, including what we’ve done to track and address today’s safety challenges as well as frontier risks.

Building on the safety evaluations and mitigations we developed for GPT-4, and GPT-4V, we’ve focused additional efforts on GPT-4o's audio capabilities which present novel risks, while also evaluating its text and vision capabilities.

Now You Can Train LLMs on a Two-Year-Old Desktop-Grade NVIDIA 3090 GPU

Recently, researchers from the School of Computer Science & Technology, Soochow University, released a research paper titled ‘MemLong: Memory-Augmented Retrieval for Long Text Modeling’, where they successfully extended an LLM’s context window from 2k to 80k tokens on two-year-old desktop-grade NVIDIA 3090 GPU.

This opens new horizons for users with limited hardware access and who still want to use AI applications locally on their computers.

Another highlight of this study was fine-tuning a 3 billion parameter version of MemLong on 0.5 billion tokens, which requires only eight 3090 GPUs for eight hours, showcasing high efficiency in resource use. This means it is not only useful for users who want to run AI applications but also for developers who want to train their models over midrange hardware.


Source-Link: analyticsindiamag

Zico Kolter Joins OpenAI’s Board of Directors

We’re strengthening our governance with expertise in AI safety and alignment. Zico will also join the Safety & Security Committee.
We’re announcing the appointment of Zico Kolter to OpenAI’s Board of Directors. As a professor and Director of the Machine Learning Department at Carnegie Mellon University, Zico’s work predominantly focuses on AI safety, alignment, and the robustness of machine learning classifiers. His research and expertise spans new deep network architectures, innovative methodologies for understanding the influence of data on models, and automated methods for evaluating AI model robustness, making him an invaluable technical director for our governance.