Update Apr 12, 2023: We have released Dolly 2.0, licensed for both research and commercial use. See the new blog post here. We show that anyone can take a dated off-the-shelf open source large language model (LLM) and give it magical ChatGPT-like instruction following ability by training it in 30 minutes on one machine, using high-quality training data. Surprisingly, instruction-following does not seem to require the latest or largest models: our model is only 6 billion parameters, compared to 175 billion for GPT-3. We open source the code for our model (Dolly) and show how it can be re-created on Databricks.

There's growing concern that artificial intelligence--namely deep learning--is becoming centralized within a few very wealthy companies. This shift does not apply to all areas of AI, but it is certainly the case for large language models, deep learning systems composed of billions of parameters and trained on terabytes of text data. Accordingly, there has been growing interest in democratizing LLMs and making them available to a broader audience. However, while there have been impressive initiatives in open-sourcing models, the hardware barriers of large language models have gone mostly unaddressed. This is one of the problems that Cerebras, a startup that specializes in AI hardware, aims to solve with its Wafer Scale processor.

The National Science Foundation and the White House's Office of Science and Technology Policy are developing a plan to make high-performance computing, machine-learning datasets and other resources more widely available to artificial intelligence researchers at every level. Developing artificial intelligence tools and technologies requires lots of data and even more computing resources. Gaining a national advantage in this area will require a significant concentration of work that is currently limited to agencies and organizations that have those resources. But the best, groundbreaking ideas aren't always centered in places with the most resources. To address this issue, the 2021 National Defense Authorization Act charged NSF and OSTP with developing a plan to build up those resources and make sure they are available to people throughout the U.S. who can make good use of them.

This article originally appeared in YFS Magazine. The art world has become a more inclusive, engaging place for newcomers and connoisseurs alike -- and we owe technology much of the credit. From machine learning and personalization to augmented reality and blockchain integrations, new technologies continue to reshape and reimagine what the art world can be and who has access to it. Many people still believe consumers won't buy art without experiencing it in-person. Online art galleries are flourishing, however, and new tools make it easier than ever for potential buyers to get up close and personal with works of art that could be thousands of miles away.

Olivier Blais of Moov.ai has had a lot of experience building AI initiatives for organizations. He's been a lot of places and is well aware of the hype and hysteria surrounding AI. He's here to help you and your company build better AI initiatives, democratize Artificial Intelligence, and alleviate some of the biggest worries your employees and customers may have about the AI revolution. The understanding of AI from a data scientist perspective is often very different from the business understanding. Democratizing your data within a business context requires you to dispel these common myths. Companies are creating these initiatives, but it's not just about being the next cool thing.

Warren, Mercer consulting's AI digital assistant, is a sophisticated AI platform designed to leverage real-time data with learned patterns to enhance workforce productivity. It works 24/7 to ensure your personal and professional obligations are well organized and your career trajectory is moving forward. It does this by contextualizing data from your past, present and future and streamlining your responsibilities and schedule in ways that encourage better decision making. In other words, Warren is your dedicated personal coach, confidant and teammate – the ultimate convergence of people and technology. Every day, people struggle to maximize the value of their time.

Machine Learning itself is not new. The concepts have been around for decades, and many companies have been building ML models and doing predictive analytics for a while. So what exactly is IBM doing in this space? I would like to say "IBM takes an enterprise approach to ML", but that sounds too vague. This post details IBM's commitment to machine learning and how our approach goes beyond hype and hand waving to offer the platforms, tools, and processes our enterprise customers need.

Having a personal assistant is often a signal that you're a member of the professional elite. The connotation is that only C-level executives and VPs are "worthy" of their own personal assistant handling their schedule.