I'm offering my theory here, why are you deleting everything, I don't understand you in any way. Write why you delete in a personal account or something or you don't like my theory...
I am not deleting Gennadiy, but moving your posts because they are off topic. Alan said in the beginning this thread would be closely Moderated. Soon I will move both of our posts to your own thread.
Yes, I'm hoping to contribute in a meaningful way. But meanwhile, my efforts to describe the LENR process have generated the usual random collection of ideas having neither logic nor continuity. I'm trying to assemble a jigsaw puzzle. The puzzle is already missing most of the pieces, which requires the shape of what is missing to be imagined. Instead of working together to find the missing parts, the other players try to hide some of the pieces
IMO, one of the main problems I see is that many of the players have invested so much of their time, reputation, and money into their ideas, they are not willing to explore other ways/theories for fear of finding they were on the wrong track all along. You, Mizuno, McKubre are some of the few willing to venture out into new territory.
Many others have formed companies to capitalize on their ideas and have investors to consider before participating in an open discussion such as thread. Others have written books, won awards, made a name for themselves in the community, which further traps them in their own self-made bubble.
But thank goodness, we now have a new crop of researchers coming into the field with mostly government funding, which eliminates much of the personal motives that have hampered LENR progress for 33 years. We also have the X-Prize (which I have heard from 2 sources will either be out "soon", or in 2024...take your pick)) which will help gain public acceptance, and accelerate R/D.
Display MoreThere is a large difference between Mills and F&P. F&P were widely replicated. Mills has not been, as far as I know.
I do not understand Mills, except for his first experiment. I understand that one, and it was replicated. It looks like conventional cold fusion to me. It is here:
Only Mills replication I know of was by NASA from his early CF days:
https://www.newenergytimes.com/v2/library/1996/1996NASA-Mills-Replication-107167.pdf
The fight water-Ni-K2CO s electrolytic cell on loan from
the Hydrocatalysis Power Corporation clearly exhibited the
phenomenon of apparent excess heat when tested at 4
selected dc currentsand one pulse mode cun'ent Dam was
collectedusingsimple' on-the-fly" calorimetric calibration
in the thermal steady state and was reduced to give the
apparent excess heat by extrapolation methods that are
accepted practice in the field of anomalous heat cell ('cold
fusion') research.
Our main findings regarding cell voltages, currents and
powers are summarized in Table I. The apparent power
gains ranged from 1.06 to 1.68. The apparent excess
power of lifts particular cell saturated at a rather low 11.4
W, at an electrical input power of 59.6 W, using a cell
current of 20 Adc, as compared to about 50 W apparent
excess reported by other workers for essentially the same
cell. We attn'bute this shortfall to an unfortunate choice of
untested nickel from an alternate source.
After switching to his Hydrino theory, there were many validations/verifications, but no replications that I am aware of.
Good read Sam. Thanks. The comments section is where the real action is. Good arguments pro/con about Mills there. Lots of references.
TTH is being respectful. He listens and then gives his own views. This is respect.
Yes, THH is respectful to everyone, and always has been. Best skeptic LENR has probably ever had because of that, and his background. His contribution to this thread has made it popular. A popular thread attracts the attention of those who really need to hear what Storms has to say, and also those in need of understanding what THH has to add.
So there is a certain synergy taking place that is beneficial to both. Storms has his audience of new researchers to pass on his knowledge, and THH...being the teacher he is, has his students to teach. I meant no disrespect for THH.
Now carry on everyone. Ed has a 4 step discovery process that he is taking you through. I find this whole thing very interesting. It is what this forum is all about.
LENR-forum Newsletter April 2023 (mailchi.mp)
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There are some on this site who adopt a sort of political tribe-based view about for or against LENR, and label contributors here according to beliefs. I don't see any need myself to do that. I am positive about some varieties of LENR (as defined here). By that I mean that the evidence published I understand and do not doubt is correctly interpreted, replicable, and positive, I am willing to be optimistic about the unknowns and hope it could be useful. That does not stop me from being cautious about all the types of evidence that don't meet that standard.
After all these years going back to the ECN's days you still don't get it. Even after being told repeatedly by the likes of Storms, Rothwell, and others that they know what they are doing, you continue on as if they are novices who need to be walked through the scientific method.
Everything you have said, or recommended, they know, and account for, or have done. They collaborate on methodology, and technique. For 33 years they have attended conferences and exchanged ideas, As has been said repeatedly, many are experts in their field, and could teach you a thing or two.
Now, Ed is part of the old guard. Like all from that era, he has "been there, done that". They do not need any more of your kind of preaching. There is however, a new crop of researchers (thanks in large part to the old guard keeping the science alive) from the USNavy, Army, NASA, HERMES, CleanHME, and many more, who may be more willing students of what you have to offer.
They are still on a learning curve, and with your solid background in what has been going on in the field (both the good, and the bad), they may learn from what you have to say.
My suggestion is to target them, and give the old timers the respect they deserve.
I
I think multiple experimental runs with 2-3 different calorimeters will give us enough statistical power for these initial experiments.
Just curious, but do you think it possible that multiple calorimeters may just give the skeptics "multiple" reasons to attack, and eventually dismiss, the results? If I have learned anything here in my time as a layman, it is that everyone, and I mean everyone, says their calorimeter is better than all those before them. That the results will be infallible. Yet, somehow, it never seems to work out that way.
So will this new shotgun approach be any different?
Oh you are pulling our legs. Buffalo do not have wings!
I always wondered how those tasty little Buffalo wings could make a buffalo fly.
Greg Gobble reported me a news about ARPA-E funding of LENR:
UC Berkeley just announced they are the lead of the ARPA-E LENR Program. Google Inc LENR patent inventors are head of the team. Odd... No mention of Google.
Just to be clear, Schenkel is not leading the ARPA-E Program. He is leading the ARPA-E funded research at both LBNL (Berkely), and the University of California (Davis). There are 6 other teams ARPA-E is funding.
That said, nice catch Greg.
I would invest now If I had the chance, tho.
Are you serious? I have been a supporter of BLP for years, but after this investor meeting, I withdraw that support as of now. How ironic if we both flip positions based on this meeting. You now a believer, and me not. But then, that is not uncommon in LENR.
If I hear from someone else in the BLP chain of command other than Mills speak, and answer the tough questions, I may reconsider. Honestly, I am sick of hearing only from Mills.
Mills responses as relayed by those in attendance were contradictory, and evasive. As soon as I read the part about why the boiler tech did not succeed, my BS meter pegged out. The comments section confirmed my suspicions.
I Gave ChatGPT an IQ Test. Here's What I Discovered - Scientific American
"So what finally did it score overall? Estimated on the basis of five subtests, the Verbal IQ of the ChatGPT was 155, superior to 99.9 percent of the test takers who make up the American WAIS III standardization sample of 2,450 people. As the chatbot lacks the requisite eyes, ears and hands, it is not able to take WAIS’s nonverbal subtests. But the Verbal IQ and Full Scale IQ scales are highly correlated in the standardization sample, so ChatGPT appears to be very intelligent by any human standards."
Also, depends on a lot of factors falling into place, but we may send one of our staff to Poland. If so, it won't be Ruby or Alan, and we will start a crowdfunding campaign to pay for airfare, hotel, and modest expenses.
This exchange however, reveals that ChatGPT is not yet an AI. It is indeed remarkable but it still can't have original ideas stemming from current knowledge. It can compare point's of view, but it can't develop it's own. It can't create new knowledge.
There seems to be some debate about whether AI can be creative. There are some examples that it can. While the experts decide that issue, I see room for the community to exploit it as it is now.
For one, it is a great research tool. Unlike other branches of science, LENR history is largely unorganized, and difficult to navigate. The few good books about it were written years ago. That, IMO, leads to unnecessary duplication of effort. This CharGPT AI will go a long way to alleviating that problem. A new researcher, or one of the ARPA-E funded teams, can be spun up on their area of interest in literally minutes, avoiding "reinventing the wheel", and wasting their time as so many others before have.
And since AI has such command of the volumes of literature scattered all over the internet in an instant, it is able to tie things together we can't. Just this one chat with Diadon taught me more about the EU, what it was, and how it might incorporate LENR, and other phenomenon, than my years of being tuned in to the subject.
And it is only going to get better at what it does.
We need to start thinking how to ingrain Asimov's three laws in every AI right now.
And they are?
IMO, this was an incredibly informative exchange between @Diadon and AI. Hope everyone reads it. I learned of this ChatGPT only a few weeks ago, and my mind is simply blown by it's potential. Not only to LENR, but all sciences.
On the other hand, if "she" gets involved in social issues, and politics, it may very well be the end of us. 
Display MoreTo learn how ChatGPT works, try asking her. She is reluctant to reveal some details. She claims she does not know. See the underlined portion below. But she describes a lot else about herself. Here is an exchange I had with her:
ME: You told me that the largest version of GPT-3 had 175 billion parameters. Is each parameter stored as 1 byte? Or some other number of bits?
ChatGPT: No, each parameter in GPT-3 is not stored as 1 byte. Instead, each parameter is typically stored as a 16-bit or 32-bit floating-point number, depending on the implementation. This is because floating-point numbers can represent a wider range of values and provide greater precision than integers.
For example, GPT-3's 175 billion parameters would require around 700 GB of memory if each parameter was stored as a 32-bit floating-point number. If each parameter was stored as a 16-bit floating-point number, it would require around 350 GB of memory.
It's worth noting that the actual memory usage of GPT-3 may be different due to various optimizations and compression techniques used by the implementation.
ME: With the 32-bit floating point number configuration, I assume all 700 GB are online in RAM. Not overlaid. Correct?
ChatGPT: No, it's not necessary for all 700 GB of GPT-3's parameters to be stored in RAM at the same time. In fact, it's not practical to store such a large model entirely in memory on most machines.
Most deep learning frameworks use a technique called "memory-mapping" to allow the model parameters to be loaded and unloaded from disk as needed during training or inference. Memory-mapping allows the model parameters to be accessed as if they were in memory, even though they are stored on disk.
In practice, the memory usage of GPT-3 will depend on the specific implementation and hardware configuration. Some implementations may use techniques like model parallelism or gradient checkpointing to reduce memory usage, while others may rely on larger hardware configurations with more memory.
ME: I meant in the "specialized computing infrastructure provided by OpenAI" that you described earlier. Is that all on-line?
ChatGPT: Ah, I see what you're asking now. Yes, in the specialized computing infrastructure provided by OpenAI, the GPT-3 model with 175 billion parameters is fully loaded into the RAM of the computing cluster during operation. The computing cluster used by OpenAI is composed of many machines with large amounts of RAM, and the model parameters are distributed across those machines.
However, it's worth noting that not all of the machines in the cluster will necessarily have a full copy of the model parameters in memory at all times. Instead, the model parameters may be partitioned across the machines and loaded into memory as needed for different parts of the computation. This approach allows the computing cluster to handle the large memory requirements of the GPT-3 model while also being scalable and efficient.
ME: I realize that memory mapping is very rapid with today's peripherals, so even an overlay configuration will be rapid.
ChatGPT: Yes, memory mapping can be very fast with modern storage devices and is commonly used to efficiently access large datasets that cannot fit entirely in memory. With memory mapping, the operating system maps a portion of a file into the virtual address space of a process, allowing the process to access the file's contents as if they were in memory.
However, it's important to note that memory-mapped I/O does have some overhead, such as page faults and disk I/O. Page faults can occur if the requested memory page is not in physical memory and must be fetched from disk, while disk I/O can occur if the requested page has not yet been read into the page cache. These overheads can be minimized through various techniques such as preloading data into the page cache, using large pages, and optimizing disk access patterns.
In any case, the actual performance of memory-mapped I/O depends on the specific hardware and software configuration, as well as the characteristics of the workload being performed.
ME: One more question about this. I read that the "specialized computing infrastructure provided by OpenAI" is an MPP configuration. Approximately how many processors are there? How many processors per parameter? I guess one GPU or TPU is equivalent to one CPU processor in a traditional MPP configuration.
ChatGPT: I'm sorry, but I don't have access to detailed information about the specific hardware and configuration of OpenAI's computing infrastructure, so I can't provide specific numbers about the number of processors or GPUs/TPUs used per parameter.
However, I can provide some general information about MPP (Massively Parallel Processing) configurations and their relationship to GPUs/TPUs.
In an MPP configuration, the processing power of a system is distributed across multiple processors or nodes, allowing for massive parallelism and high throughput. This is typically achieved through the use of specialized hardware and software, such as high-speed interconnects and parallel file systems.
In the context of deep learning, GPUs and TPUs are often used as the primary processing units in MPP configurations due to their ability to perform large-scale matrix operations in parallel. GPUs and TPUs are designed with a large number of processing cores that can perform these operations simultaneously, making them well-suited for deep learning workloads.
The number of processors or GPUs/TPUs used per parameter will depend on various factors, such as the size of the model, the complexity of the workload, and the hardware configuration. In general, larger models and more complex workloads will require more processing power per parameter, while smaller models and simpler workloads may be able to make do with less.
ME: So, a larger model actually needs a higher ratio of GPU or TPU units per parameter? A model is "larger" in the sense that it does more processing for each parameter, not that it has more parameters. That is interesting.
ChatGPT: Yes, you're correct. The number of processing units, whether CPUs, GPUs or TPUs, required per parameter in a deep learning model depends not only on the number of parameters but also on the complexity of the computation required for each parameter.
Larger models, in terms of the computation required for each parameter, will require more processing units per parameter, as the computation becomes more computationally intensive. This is especially true for models that require more complex operations, such as recurrent neural networks, convolutional neural networks, and transformers like GPT-3.
In general, the number of processing units required per parameter is proportional to the computational requirements of the model. Therefore, larger models require a higher ratio of processing units per parameter, which means that more powerful hardware configurations, such as clusters of GPUs or TPUs, are needed to efficiently train or inference these models.
Hmmm...interesting. It is like you two are developing a relationship. Professional of course.
We will be linking to this thread in our April newsletter. The staff believes this is a significant new development the LENR community may be able take advantage of. Many thanks to David Nygren for bringing it up.
I thought the consensus of opinion here is that the Lugano results were an experimental error, caused by measuring emissivity wrong. Is that not the case?
I think that is what he was getting at.
Found out that Christy at IE will accept credit card payments over the phone.
I’d like to start a thread where we can share any kind of information on commercial or academic LENR projects with known or estimated power results. As an example BEC’s 2018 SRI report states an XSH output of around 5-6W and COP around 1.5-2. It’s been five years since this has been updated so I expect them to be far ahead of that by now.
I will try to compile replication data and original data from Mizuno himself for my part including keeping my promise to Bruce to post original calibration and test data.
I am hoping that researchers globally would feel comfortable enough to post results here for public scrutiny which benefits all of us. As far as I can tell no such thread currently exists.
Good topic for discussion. Surprised no takers yet. Perhaps there is some confusion as to what you mean by "confirmed LENR results"? We on the forum have debated that before, and as I remember, it never seems to resolve in a consensus. Understandable, as few discoveries in their infancy do.
