Inderdaad, ik ben ook voor die marketing gevallen.
Denk je dat mmWave binnenkort beschikbaar zal zijn? Op dit moment heb ik een sub-6 GHz 5G-router voor internet thuis. Ik ben benieuwd hoe een mmWave-router zou presteren als er een zendmast in de buurt is.
Publishing in a more prestigious journal usually means that your work will be read by a greater number of people. The journal that a paper is published on carries weight on the CV, and it is a relevant parameter for committees reviewing a grant applicant or when evaluating an academic job applicant.
Someone who is able to fund their own research can get away with publishing to a forum, or to some of the Arxivs without submitting to a journal. But an academic that relies on grants and benefits from collaborations is much more likely to succeed in academia if they publish in academic journals. It is not necessarily that academics want to rely on publishers, but it is often a case of either you accept and adapt to the system or you don't thrive in it.
It would be great to find an alternative that cuts the middle man altogether. It is not a simple matter to get researchers to contribute their high-quality work to a zero-prestige experimental system, nor is it be easy to establish a robust community-driven peer-review system that provides a filtering capacity similar to that of prestigious journals. I do hope some alternative system manages to get traction in the coming years.
I did not know of the term "open washing" before reading this article. Unfortunately it does seem like the pending EU legislation on AI has created a strong incentive for companies to do their best to dilute the term and benefit from the regulations.
There are some paragraphs in the article that illustrate the point nicely:
In 2024, the AI landscape will be shaken up by the EU's AI Act, the world's first comprehensive AI law, with a projected impact on science and society comparable to GDPR. Fostering open source driven innovation is one of the aims of this legislation. This means it will be putting legal weight on the term “open source”, creating only stronger incentives for lobbying operations driven by corporate interests to water down its definition.
[.....] Under the latest version of the Act, providers of AI models “under a free and open licence” are exempted from the requirement to “draw up and keep up-to-date the technical documentation of the model, including its training and testing process and the results of its evaluation, which shall contain, at a minimum, the elements set out in Annex IXa” (Article 52c:1a). Instead, they would face a much vaguer requirement to “draw up and make publicly available a sufficiently detailed summary about the content used for training of the general-purpose AI model according to a template provided by the AI Office” (Article 52c:1d).
If this exemption or one like it stays in place, it will have two important effects: (i) attaining open source status becomes highly attractive to any generative AI provider, as it provides a way to escape some of the most onerous requirements of technical documentation and the attendant scientific and legal scrutiny; (ii) an as-yet unspecified template (and the AI Office managing it) will become the focus of intense lobbying efforts from multiple stakeholders (e.g., [12]). Figuring out what constitutes a “sufficiently detailed summary” will literally become a million dollar question.
Thank you for pointing out Grayjay, I had not heard of it. I will look into it.
Some time last year I learned of an example of such a project (peerreview on GitHub):
The goal of this project was to create an open access "Peer Review" platform:
Peer Review is an open access, reputation based scientific publishing system that has the potential to replace the journal system with a single, community run website. It is free to publish, free to access, and the plan is to support it with donations and (eventually, hopefully) institutional support.
It allows academic authors to submit a draft of a paper for review by peers in their field, and then to publish it for public consumption once they are ready. It allows their peers to exercise post-publish quality control of papers by voting them up or down and posting public responses.
I just looked it up now to see how it is going... And I am a bit saddened to find out that the developer decided to stop. The author has a blog in which he wrote about the project and about why he is not so optimistic about the prospects of crowd sourced peer review anymore: https://www.theroadgoeson.com/crowdsourcing-peer-review-probably-wont-work , and related posts referenced therein.
It is only one opinion, but at least it is the opinion of someone who has thought about this some time and made a real effort towards the goal, so maybe you find some value from his perspective.
Personally, I am still optimistic about this being possible. But that's easy for me to say as I have not invested the effort!
Cross-posting to the OpenSource community as I think this topic will also be of interest here.
This is an analysis of how "open" different open source AI systems are. I am also posting the two figures from the paper that summarize this information below.
ABSTRACT
The past year has seen a steep rise in generative AI systems that claim to be open. But how open are they really? The question of what counts as open source in generative AI is poised to take on particular importance in light of the upcoming EU AI Act that regulates open source systems differently, creating an urgent need for practical openness assessment. Here we use an evidence-based framework that distinguishes 14 dimensions of openness, from training datasets to scientific and technical documentation and from licensing to access methods. Surveying over 45 generative AI systems (both text and text-to-image), we find that while the term open source is widely used, many models are ‘open weight’ at best and many providers seek to evade scientific, legal and regulatory scrutiny by withholding information on training and fine-tuning data. We argue that openness in generative AI is necessarily composite (consisting of multiple elements) and gradient (coming in degrees), and point out the risk of relying on single features like access or licensing to declare models open or not. Evidence-based openness assessment can help foster a generative AI landscape in which models can be effectively regulated, model providers can be held accountable, scientists can scrutinise generative AI, and end users can make informed decisions.
Figure 2 (click to enlarge): Openness of 40 text generators described as open, with OpenAI’s ChatGPT (bottom) as closed reference point. Every cell records a three-level openness judgement (✓ open, ∼ partial or ✗ closed). The table is sorted by cumulative openness, where ✓ is 1, ∼ is 0.5 and ✗ is 0 points. RL may refer to RLHF or other forms of fine-tuning aimed at fostering instruction-following behaviour. For the latest updates see: https://opening-up-chatgpt.github.io
Figure 3 (click to enlarge): Overview of 6 text-to-image systems described as open, with OpenAI's DALL-E as a reference point. Every cell records a three-level openness judgement (✓ open, ∼ partial or ✗ closed). The table is sorted by cumulative openness, where ✓ is 1, ∼ is 0.5 and ✗ is 0 points.
There is also a related Nature news article: Not all ‘open source’ AI models are actually open: here’s a ranking
PDF Link: https://dl.acm.org/doi/pdf/10.1145/3630106.3659005
ABSTRACT
The past year has seen a steep rise in generative AI systems that claim to be open. But how open are they really? The question of what counts as open source in generative AI is poised to take on particular importance in light of the upcoming EU AI Act that regulates open source systems differently, creating an urgent need for practical openness assessment. Here we use an evidence-based framework that distinguishes 14 dimensions of openness, from training datasets to scientific and technical documentation and from licensing to access methods. Surveying over 45 generative AI systems (both text and text-to-image), we find that while the term open source is widely used, many models are ‘open weight’ at best and many providers seek to evade scientific, legal and regulatory scrutiny by withholding information on training and fine-tuning data. We argue that openness in generative AI is necessarily composite (consisting of multiple elements) and gradient (coming in degrees), and point out the risk of relying on single features like access or licensing to declare models open or not. Evidence-based openness assessment can help foster a generative AI landscape in which models can be effectively regulated, model providers can be held accountable, scientists can scrutinise generative AI, and end users can make informed decisions.
Figure 2 (click to enlarge): Openness of 40 text generators described as open, with OpenAI’s ChatGPT (bottom) as closed reference point. Every cell records a three-level openness judgement (✓ open, ∼ partial or ✗ closed). The table is sorted by cumulative openness, where ✓ is 1, ∼ is 0.5 and ✗ is 0 points. RL may refer to RLHF or other forms of fine-tuning aimed at fostering instruction-following behaviour. For the latest updates see: https://opening-up-chatgpt.github.io
Figure 3 (click to enlarge): Overview of 6 text-to-image systems described as open, with OpenAI's DALL-E as a reference point. Every cell records a three-level openness judgement (✓ open, ∼ partial or ✗ closed). The table is sorted by cumulative openness, where ✓ is 1, ∼ is 0.5 and ✗ is 0 points.
There is also a related Nature news article: Not all ‘open source’ AI models are actually open: here’s a ranking
PDF Link: https://dl.acm.org/doi/pdf/10.1145/3630106.3659005
The bottle is a carbon dioxide tank. It is connected to a regulator that can open/close the valve to let CO2 out. During the day it brings the CO2 level under the leaves to around 800 - 1000 parts per million (ppm). Usually the level in the air is closer to 400 - 500 ppm, and fast growing plants can grow faster with some extra CO2 in the air to build into sugars during photosynthesis. At least in theory... For me it is an experiment in CO2 regulation as I have measured and decreased CO2 levels in the past (when growing mushrooms and tempeh) but I had never actively delivered it, so I thought this would be a good opportunity to learn.
It turns out pumpkin flowers are very fragrant, and the odor is very pleasant, but I am not good at describing smells with words, sorry... To me it smells like a mixture of a rhododendron flower and a pumpkin. I recently went to a wedding in which they served ricotta stuffed zuccini flowers (very similar flowers) and the cook clearly knew what she was doing, in that case the zuccini flowers still had some of the fragrance and this made the dish taste very special. In my attempt I filled the flowers with some curry rice and then pan-seared them in butter, and all the fragrance went away in the process. So the flower was just a vessel with the soft texture of a petal and the taste of browned butter. I did not succeed in keeping any flower flavor. It was a quick-and-dirty experiment... I would like to learn more about cooking with flowers while keeping some flavor.
We are having a pumpking growing competition at work and I live in an apartment, so I'm working with what I have 😆
The plant already produced many male flowers. From what I have read, the male flowers usually come out 10 - 14 days before the female flowers. They open up for a single day and then they close and fall off.
I found out that tey are edible, so I stuffed a few of them with some left overs as a culinary experiment.
And the first female flower has arrived!
I think that they are referring to Paxillus involotus
It is quite an interesting mushroom. It was considered "safe to eat" for a long time, but it contains an antigen that a human's immune system can learn to attack.
The antigen is still of unknown structure but it stimulates the formation of IgG antibodies in the blood serum.
I once looked into whether this immune response builds up over many exposures, or if it is a random event that has a probability of happening for each exposure. I don't remember finding a convincing answer... If it is a random event, then mushroom could be considered a "Russian roulette" mushroom that will usually provide a nice meal, but, if unlucky, you may experience the following:
Poisoning symptoms are rapid in onset, consisting initially of vomiting, diarrhea, abdominal pain, and associated decreased blood volume. Shortly after these initial symptoms appear, hemolysis develops, resulting in reduced urine output, hemoglobin in the urine or outright absence of urine formation, and anemia. Hemolysis may lead to numerous complications including acute kidney injury, shock, acute respiratory failure, and disseminated intravascular coagulation. These complications can cause significant morbidity with fatalities having been reported.
I agree with you that this is probably unrelated to the "generally similar to humans" comment. I feel like this fantasy is a combination of the above fact mixed in with the fact that the Fungi belong to the Opisthokonts, which places them closer to animals than plants, and so they share some interesting cellular characteristics with us. This places them closer to animals than plants, but "generally similar to humans" is perhaps a bit of a stretch ^_^
But, it is just a meme about a guy being hyped about mushrooms. Hopefully people don't expect memes to be super accurate 😁
This feels like one of those chain messages that we would get on Facebook asking us to do something like posting "I don't give permission to Facebook to use my data". Except that this time it is actually true!
I have added "_nomap" to my SSID and now I have to read the manual for the wifi extender, which by default appends _EXT to the SSID 🙄
I would much rather see a "_yesmap" opt-in policy!
Ahh, wish I could join you! But I am travelling these days. Enjoy Canvassing!