The other day I posted the message below, describing recent progress in AI. An aspect of this may be instructive to cold fusion researchers.
This recent progress has various causes. One of the main ones is a dramatic improvement in the neural network technique. (See https://www.nytimes.com/2016/12/14/magazine/the-great-ai-awakening.html and many other articles.) The neural network AI technique has been around for decades. It did not work well in the past because the programs used a single network. Nowadays they use multiple networks, in layers, where one layer feeds output to another layer. Here is the lesson for cold fusion. There may be techniques in cold fusion that have been abandoned which, with some modification, might work well. For example, we assume that Pd-D cold fusion has no future because palladium is so expensive. Perhaps this is not such a limitation. As I pointed out in the past, thin film Pd is used in catalytic converters, where it is exposed to a fairly large fraction of all of the heat produced in the world. If there is enough Pd for this application, perhaps there would be enough to produce a large fraction of world energy with similar thin-film Pd. Many techniques have been described in the literature that worked a few times spectacularly, but most of the time they do not work. They are irreproducible. The SuperWave technique once produced, "Excess Power of up to 34 watts; Average ~20 watts for 17 h." ( http://www.lenr-canr.org/acrobat/DardikIexcessheat.pdf) I have heard that despite strenuous efforts, it has never done that at U. Missouri. Does that mean the technique is flawed? Hopelessly irreproducible? Maybe. But perhaps with a modification or extension it will work, just as the neural network technique began to work when it was extended to multiple levels. Adding levels to neural networks was not such a big change, conceptually. In retrospect, it seems like a natural extension of the technique. It may be how naturally occurring neural networks in the brain work. There might some analogous "natural" extension to the SuperWave technique that will dramatically improve it. Or there might be something about the earlier, more successful experiments that has been overlooked, or forgotten. Neural network computing was denigrated during the long period now called the AI winter, when the research reached a nadir, around 1990. Techniques that have now been demonstrated to work were dismissed at that time. Some were not given a good enough chance. Others may have been ahead of their time, meaning the could not work without today's massively larger hardware. Along similar lines, I expect there are many new tools and technologies available now that would benefit cold fusion, that were not available in the 1990s. Along the same lines, a technique or a material that cannot work at one stage in the development of a technology might suddenly come into its own a short while later. Transistors began with germanium. Silicon would not have worked at first, because of various limitations. Silicon began to work in 1954 and rapidly replaced germanium. In aviation, people assume that the propeller is old technology that has been superseded. That is not true. Modern fan-jet engines incorporate propellers. Propellers were used for a while, and then put aside, and then used again. It is a complicated history that I described briefly on p. 2 here: http://lenr-canr.org/acrobat/RothwellJtransistora.pdf Quoting an aviation historian: ". . . the commercial development of the turbine passed through some paradoxical stages before arriving at the present big jet era. Contrary to one standard illusion, modern technology does not advance with breathtaking speed along a predictable linear track. Progress goes hesitantly much of the time, sometimes encountering long fallow periods and often doubling back unpredictably upon its path." ---------- Forwarded message ---------- Progress in AI seems to be accelerating, according to a paper in *Nature* from the AI people at Google. See: http://www.slate.com/blogs/future_tense/2017/10/18/ google_s_ai_made_some_pretty_huge_leaps_this_week.html They developed a new version of their go-playing program, called AlphaGo Zero. Features: Self-training. No use of existing datasets. Efficient. It uses only 4 processors. The previous version used 48. Effective. This one beat the old program in 100 to zero matches. (The old program beat the world's best go player last year). Quote: "This version had taught itself how to play the game. All on its own, given only the basic rules of the game. (The original, by comparison, learned from a database of 100,000 Go games.) According to Google’s researchers, AlphaGo Zero has achieved superhuman-level performance: It won 100–0 against its champion predecessor, AlphaGo." The same technology is being used to develop software modules. They work better than human-written modules. Quote: ". . . [R]esearchers announced that Google’s project AutoML had successfully taught itself to program machine learning software on its own. While it’s limited to basic programming tasks, the code AutoML created was, in some cases, better than the code written by its human counterparts. In a program designed to identify objects in a picture, the AI-created algorithm achieved a 43 percent success rate at the task. The human-developed code, by comparison, only scored 39 percent on the task."
