Artificial Intelligence has become very present in the media in the last couple of years. At the end of 2022, ChatGPT has captured the world’s attention, showing at least a hundred million users around the globe the extraordinary potential of large language models. Large language models such as LLaMA, Bard and ChatGPT mimic intelligent behavior equivalent to, or indistinguishable from, that of a human in specific areas (i.e., Imitation Game or Turing Test). Stephen Wolfram has written an article about how ChatGPT works.
Year-end 2022 might therefore be a watershed moment for human mankind since Artificial Intelligence has now the potential to change the way how humans think and work
… All these achievements have one thing in common – they are build on a model using an Artificial Neural Networks (ANN). … ANN are very good function approximators provided that big data of the corresponding domain is available. Almost all practical problems such as playing a game of Go or mimic intelligent behavior can be represented by mathematical functions.
The corresponding theorem that formulates this basic idea of approximation is called Universal Approximation Theorem. It is a fundamental result in the field of ANN, which states that certain types of neural network can approximate certain function to any desired degree of accuracy. This theorem suggest that a neural network is capable of learning complex patterns and relationships in data as long as certain conditions are fulfilled.
The Universal Approximation Theorem is the root-cause why ANN are so successful and capable in solving a wide range of problems in machine learning and other fields. — Read More
Monthly Archives: April 2024
Microsoft AI creates scary real talkie videos from a single photo
Microsoft Research Asia has revealed an AI model that can generate frighteningly realistic deepfake videos from a single still image and an audio track. How will we be able to trust what we see and hear online from here on in?
… After training the [VASA-1] model on footage of around 6,000 real-life talking faces from the VoxCeleb2 dataset, the technology is able to generate scary real video where the newly animated subject is not only able to accurately lip-sync to a supplied voice audio track, but also sports varied facial expressions and natural head movements – all from a single static headshot photo. — Read More
Google Consolidates AI-Building Teams Across Research and DeepMind
Google is consolidating the teams that focus on building artificial intelligence (AI) models across Google Research and Google DeepMind.
All this work will now be done within Google DeepMind, Sundar Pichai, CEO of Google and Alphabet, said in a note to employees posted on the company’s website Thursday (April 18). — Read More
Introducing Meta Llama 3: The most capable openly available LLM to date
Today, we’re excited to share the first two models of the next generation of Llama, Meta Llama 3, available for broad use. This release features pretrained and instruction-fine-tuned language models with 8B and 70B parameters that can support a broad range of use cases. This next generation of Llama demonstrates state-of-the-art performance on a wide range of industry benchmarks and offers new capabilities, including improved reasoning. We believe these are the best open source models of their class, period. In support of our longstanding open approach, we’re putting Llama 3 in the hands of the community. We want to kickstart the next wave of innovation in AI across the stack—from applications to developer tools to evals to inference optimizations and more. We can’t wait to see what you build and look forward to your feedback.
Llama 3 models will soon be available on AWS, Databricks, Google Cloud, Hugging Face, Kaggle, IBM WatsonX, Microsoft Azure, NVIDIA NIM, and Snowflake, and with support from hardware platforms offered by AMD, AWS, Dell, Intel, NVIDIA, and Qualcomm. — Read More
Newsweek is making generative AI a fixture in its newsroom
If you scroll down to the end of almost any article on Newsweek.com right now — past the headline, the article copy, several programmatic ads, and the author bio — you’ll find a short note. “To read how Newsweek uses AI, click here,” reads the text box. The link leads to Newsweek’s editorial standards page, where several paragraphs now outline how generative AI tools are being folded into the publication’s editorial process.
The disclosure is just one signal of a larger experiment with AI-assisted editorial work happening right now at the 90-year-old brand. — Read More
Effort
With Effort you can adjust smoothly – and in real time – how many calculations you’d like to do during inference of an LLM model.
At 50% calculations it is as fast as regular matrix multiplications on Apple Silicon chips. At 25% effort it’s twice as fast and still retains most of the quality.
You can also freely choose to skip loading the least important weights.
It is implemented for Mistral now, it should work for all the other models just as well. No retraining needed, just conversion to a different format and some precomputation. — Read More
Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention
This work introduces an efficient method to scale Transformer-based Large Language Models (LLMs) to infinitely long inputs with bounded memory and computation. A key component in our proposed approach is a new attention technique dubbed Infini-attention. The Infini-attention incorporates a compressive memory into the vanilla attention mechanism and builds in both masked local attention and long-term linear attention mechanisms in a single Transformer block. We demonstrate the effectiveness of our approach on long-context language modeling benchmarks, 1M sequence length passkey context block retrieval and 500K length book summarization tasks with 1B and 8B LLMs. Our approach introduces minimal bounded memory parameters and enables fast streaming inference for LLMs. — Read More
#nlpHow AI adds to human potential
Generative AI is advancing at a breakneck pace, prompting questions on risk and opportunity, from content creation to personal data management. In a special live recording, we delve into the ways AI can augment human work and spur innovation, instead of simply using AI to cut costs or replace jobs. Host Jeff Berman joined a seasoned AI researcher, Intel’s Lama Nachman, and a young start-up founder, Scale AI’s Alexandr Wang, on stage at the Intel Vision event in April 2024. They explore topics like AI’s disruption of creative industries, mitigating its biggest risks (like deep fakes), and why human critical thinking will be even more vital as AI technology spreads. — Read More
UMD-LinkUp AI Maps Transforms AI Job Tracking
UMD-LinkUp, a collaboration between the Robert H. Smith School of Business at the University of Maryland, LinkUp Job Market Data, and Outrigger Group, introduced the world’s first tool for mapping the creation of jobs requiring artificial intelligence skills: UMD-LinkUp AI Maps.
AI Maps leverages LinkUp’s industry-leading job data to visualize the spread of jobs requiring skills in AI across the country – by sector, state and more granular geographic levels. The resulting interactive map allows users to track the creation of U.S.-based AI jobs each month; rank states by their share of those jobs; do a deeper dive across economic sectors, metropolitan areas, and counties; and determine a region’s AI Intensity: the ratio of its AI jobs to all other postings. — Read More
Rick's Cafe AI 