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Home Innovations & Investing

We Need Open Artificial Intelligence

Machine learning models are not software.

Heather Meeker by Heather Meeker
January 10, 2024
in Innovations & Investing
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We Need Open Artificial Intelligence

Possessed Photography

Machine learning has been around for a long time. But in late 2022, recent advancements in deep learning and large language models started to change the game and come into the public eye. And people started thinking, “We love Open Source software, so, let’s have Open Source AI, too.”

But what is Open Source AI? And the answer is: we don’t know yet.

Machine learning models are not software. Software is written by humans, like me. Machine learning models are trained; they learn on their own automatically, based on the input data provided by humans. When programmers want to fix a computer program, they know what they need: the source code. But if you want to fix a model, you need a lot more: software to train it, data to train it, a plan for training it, and so forth. It is much more complex. And reproducing it exactly ranges from difficult to nearly impossible.

The Open Source Definition, which was made for software, is now in its third decade, and has been a stunning success. There are standard Open Source licenses that everyone uses. Access to source code is a living, working concept that people use every day. But when we try to apply Open Source concepts to AI, we need to first go back to principles.

For something to be “Open Source” it needs to have one overarching quality:  transparency. What if an AI is screening you for a job, or for a medical treatment, or deciding a prison sentence? You want to know how it works. But deep learning models right now are a black box. If you look at the output of a model, it’s impossible to tell how or why the model came up with that output. All you can do is look at the inputs to see if its training was correct. And that’s not nearly as straightforward as looking at source code.

AI has the potential to greatly benefit our world. Now is the first time in history we’ve had the information and technology to tackle our biggest problems, like climate change, poverty and war. Some people are saying AI will destroy the world, but I think it contributes to the hope of saving the world.

But first, we need to trust it. And to trust it, it needs to be open and transparent.

As a consumer, you should demand that the AI you use is open. As a developer, you should know what rights you have to study and improve AI. As a voter, you should have the right to demand that AI used by the government is open and transparent.

Without transparency, AI is doomed. AI is potentially so powerful and capable that people are already frightened of it. Without transparency, AI risks going the way of crypto–a technology with great potential that gets shut down by distrust. I hope that we will figure out how to guarantee transparency before that happens, because the problems AI can help us solve are urgent, and I believe we can solve them if we work together.

Source: Voices of Open Source
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Heather Meeker

Heather Meeker

Heather Meeker is also a Founding Portfolio Partner at OSS Capital, an early-stage venture capital fund specializing in commercial open source development. She was formerly a partner in O’Melveny & Myers’ Silicon Valley office in the Mergers and Acquisitions practice group.

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Videos

In this episode of the Daily Remedy Podcast, Tiffany Ryder discusses her insights on healthcare messaging, the impact of COVID-19 on patient trust, and the importance of transparency in health policy. She emphasizes the need for clear communication in the face of divisiveness and explores the complexities surrounding the estrogen debate. Additionally, Tiffany highlights positive developments in health policy and the necessity of effectively conveying these changes to the public.

Tiffany Ryder is a political commentator and public health policy thought leader who publishes the Substack newsletter Signal and Noise: https://signalandnoise.online/


Chapters

00:00 Introduction to Healthcare Conversations
02:58 Signal and Noise: Understanding Healthcare Communication
05:56 The Storytelling Problem in Healthcare
08:58 Navigating Political Divisiveness in Health Policy
11:55 The Role of Media in Health Policy
15:03 Bias in Health Reporting
17:56 Estrogen and Health Policy: A Case Study
24:00 Positive Developments in Health Policy
27:03 Looking Ahead: Future of Health Policy
31:49 Communicating Health Policy Effectively
The Impact of COVID-19 on Patient Trust
YouTube Video ujzgl7HDlsw
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2027 Medicare Advantage & Part D Advance Notice

Clinical Reads

GLP-1 Drugs Have Moved Past Weight Loss. Medicine Has Not Fully Caught Up.

Glucagon-Like Peptide–Based Therapies and Longevity: Clinical Implications from Emerging Evidence

by Daily Remedy
March 1, 2026
0

Glucagon-like peptide–based therapies are increasingly used for weight management and glycemic control, but their potential impact on long-term survival remains uncertain. The clinical question addressed in this report is whether treatment with glucagon-like peptide receptor agonists is associated with reductions in all-cause mortality and age-related morbidity beyond their established metabolic effects. This question matters because these agents are now prescribed across broad patient populations, including individuals without diabetes, and long-term exposure may influence cardiovascular, oncologic, and neurodegenerative outcomes. Understanding whether...

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