memory - York Jong on Diigo

06 May 09

透視記憶

本書為美國精神病學、神經科學、心理學教授史奎爾(Larry R. Squire)及二○○○年因研究海蝸牛機制而獲諾貝爾生醫獎得主肯戴爾(Eric R. Kandel)第一本有關記憶研究的革命性發現與看法，除了了解記憶是如何作用，並從分子生物學的領域中探究大腦的記憶系統與認知記憶儲存機制，從而尋求研發治療因年齡而造成記憶衰退的藥物，以及減緩阿茲海默症病情惡化的治療法，對於未來神經學和精神病學在醫學上的研究和臨床治療，將有莫大的幫助與影響力。

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book choice brain neuron memory

11 Mar 09

被灌水的 Python

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Python memory

17 Feb 08

《机器人》第三章－能力和显现

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5star AI book computer memory robot trend

三十年的计算机视觉的历程表明，1 MIPS的机器能从实时图像中提取简单的特征量───在杂色背景里跟踪白线或白点。10 MIPS的机器能跟随复杂的灰度区，巡航导弹、灵巧炸弹和早期的自驾驶大蓬车证实了这点。100 MIPS的机器恰好能追踪路面上不可预测的特征量，Navlab在长距离的行程证实了这点。1000 MIPS的机器完全能识别三维空间中带纹理的粗糙物体，某些中分辨率的立体视觉程序解释了这个问题，也包括我的程序。几个“垃圾箱挖掘”程序认为，10000 MIPS的机器能从杂乱中找到三维物体，并且高分辨率的立体视觉程序用10 MIPS的机器在1小时内演示了这一点。随着计算机速度和内存的增大，数据量问题逐渐不再是问题。
除了纯粹的运算规模，也得考虑其他因素。对1 MIPS的机器，最适用的程序是能有效处理检测数据的手工程序。
12 more annotations...
- 机器人视觉程序想要聪相应图像中得到一个边缘检测或移动检测数据的话，需执行100条计算机指令。一百万次检测则对应执行一亿条指令，1000MIPS的机器1秒能重复十次上述过程，基本上可与人的视网膜相比，新型高端个人计算机刚好能达到这个性能。
- 程序的实现不但取决于机器的速度，而且还受内存的制约。令人惊奇的是，在计算机发展史中，内存和速度比是非常恒定的
- 我们将内存容量除以速度定义为“时间常数”，粗略表示计算机扫描内存一次需要的时间。1兆字节/1 MIPS表示的结果是1秒。若机器的内存不能满足速度要求，这是新机器典型的情况，似乎运算速度很快，但会不必要地局限于小程序。若机器内存相对速度过剩，可以说它的商业可行性到了尽头，虽能运算大程序，但出奇的慢
- 先前已得出一亿MIPS能模仿人的功能这一结论，在这里做比较的话，前面所述的兆字节/MIPS的原则似乎在神经系统中也成立！交互的机器好象是神经系统的外延，遵从同样的时间常数。与其用机器与外界交互，不如让不同速度/存储比的人来完成
- 视听处理器和高性能飞行器等的控制器需要反就迅速，所以每兆字节相应很多MIPS。自动数据库和时变的安全摄像等问题涉及到慢速机器，每个MIPS会对应很多兆字节
- 飞虫似乎比人类反应快好几倍，MIPS的相对量要大一些。动物的情况也是这样，细胞间靠电化学和酶的方式传递信号。尽管植物细胞与动物神经元有本质的区别，一些植物细胞间似乎也能通讯。有一天，我们可能发现植物能记忆许多信息，但处理缓慢
- 二战后，计算机容量每两年翻一番。但进入20世纪80年代，每18个月计算机性能就要翻倍，20世纪90年代后期似乎缩短为12个月
- 通用性的实现是需要代价的。一个通用机器需要十倍或十倍以上的资源来完成一项专用任务。如果任务改变，通用机器可以被重新编程，而专用机器就得被淘汰。
- 以目前的发展速度，类似人的机器人所需要的计算机将在21世纪20年代出现。
- 实现高级计算的关键是微型化，因为小部件的惯性小，在同等能量下运行更快，在给定空间中能集成更多内容
- 集成芯片不但在持续发展，而且在加速前进。制作集成电路的短波长的光被替代，制作中使用了更精确方法来嵌入混杂成份。集成电路的电压在减小，采用了更好的绝缘体、屏蔽方法、散热手段，电路上出现了更有效的晶体管、更密的管腿模式和不辐射的包装材料。如果有钱来刺激的话，总会有办法。事实上，方法在实验室里早有了。因为工程师们当时还没注意这些，他们正在完善已有的技术。当根本问题出现，他们才感到忧虑。当需求的呼声很高时，巨大研究成果会转变为现实的生产。
- 传统晶体管电路依赖于大量电子的流动，当规模很小时，会引起状态波动。但采用单电子晶体管和量子设备可以解决这项难题，它们的工作原理是量子波干涉。这些新设备越小，工作得就越好。干涉图式很适用目前的电路规模，它只用很小能量就可将电子从一边撞到另一边，从而完成操作。因此，这些电路可以在温度很低的环境下工作。这种方法能集成到0.01微米，量子交换在室温下就能进行。

08 Feb 08

最快速度找到内存泄漏

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CPP VC memory

15 Jan 08

Thinker: 資源管理與程式

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C memory programming

001 int foo() {
002 char *p1, *p2, *p3;
003
004 p1 = p2 = p3 = NULL;
005
006 do {
007 p1 = (char *)malloc(...);
008 if(p1 == NULL) break;
009 if(sub1(p1) == ERR) break;
010
011 p2 = (char *)malloc(...);
012 if(p2 == NULL) break;
013 if(sub2(p1, p2) == ERR) break;
014
015 p3 = (char *)malloc(...);
016 if(p3 == NULL) goto error;
017 if(sub3(p1, p2, p3) == ERR) break;
018
019 free(p1); free(p2); free(p3);
020 return OK;
021 } while(0);
022
023 if(p1) free(p1);
024 if(p2) free(p2);
025 if(p3) free(p3);
026 return ERR;
027 }
001 static int _foo(char *p1, char *p2, char *p3) {
002 if(sub1(p1) == ERR) return ERR;
003 if(sub2(p1, p2) == ERR) return ERR;
004 if(sub3(p1, p2, p3) == ERR) return ERR;
005 return OK;
006 }
007
008 #define xfree(p) (void)((p)? free(p): NULL)
009 int foo() {
010 char *p1, *p2, *p3;
011 int r;
012
013 p1 = (char *)malloc(...);
014 p2 = (char *)malloc(...);
015 p3 = (char *)malloc(...);
016
017 if(p1 && p2 && p3)
018 r = _foo(p1, p2, p3);
019
020 xfree(p1); xfree(p2); xfree(p3);
021 }

09 Sep 07

When will computer hardware match the human brain? by Hans Moravec

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5star AI MIPS brain computer megabytes memory trend

The processing power and memory
capacity necessary to match general intellectual performance
of the human brain are estimated. Based on extrapolation of
past trends and on examination of technologies under
development, it is predicted that the required hardware will
be available in cheap machines in the 2020s.
Programs need memory as well as processing speed to do
their work. The ratio of memory to speed has remained
constant during computing history.
15 more annotations...
- Dividing memory
  by speed defines a "time constant," roughly how
  long it takes the computer to run once through its
  memory. One megabyte per MIPS gives one second, a nice
  human interval.
- Machines with less memory for their
  speed, typically new models, seem fast, but unnecessarily
  limited to small programs. Models with more memory for
  their speed, often ones reaching the end of their run,
  can handle larger programs, but unpleasantly slowly.
- Customers maintain the ratio by asking
  "would the next dollar be better spent on more speed
  or more memory?"
- The megabyte/MIPS ratio seems to hold
  for nervous systems too! The contingency is the other way
  around: computers are configured to interact at human
  time scales, and robots interacting with humans seem also
  to be best at that ratio.
- faster
  machines, for instance audio and video processors and
  controllers of high-performance aircraft, have many MIPS
  for each megabyte. Very slow machines, for instance
  time-lapse security cameras and automatic data libraries,
  store many megabytes for each of their MIPS.
- Flying
  insects seem to be a few times faster than humans, so may
  have more MIPS than megabytes. As in animals, cells in
  plants signal one other electrochemically and
  enzymatically. Some plant cells seem specialized for
  communication, though apparently not as extremely as
  animal neurons. One day we may find that plants remember
  much, but process it slowly
- With our conversions, a 100-MIPS robot, for instance
  Navlab, has mental power similar to a 100,000-neuron
  housefly. The following figure rates various entities.
- MIPS and Megabytes.
- Universal computers
  can imitate other entities at their location in the
  diagram, but the more specialized entities cannot. A
  100-million-MIPS computer may be programmed not only to
  think like a human, but also to imitate other
  similarly-sized computers. But humans cannot imitate
  100-million-MIPS computers--our general-purpose
  calculation ability is under a millionth of a MIPS.
- Computers doubled in capacity every two years after the war, a
  pace that became an industry given: companies that wished to grow
  sought to exceed it, companies that failed to keep up lost
  business. In the 1980s the doubling time contracted to 18 months,
  and computer performance in the late 1990s seems to be doubling
  every 12 months.
- Faster than Exponential Growth in
  Computing Power. The number of MIPS in $1000 of
  computer from 1900 to the present. Steady improvements in
  mechanical and electromechanical calculators before World War II
  had increased the speed of calculation a thousandfold over manual
  methods from 1900 to 1940. The pace quickened with the appearance
  of electronic computers during the war, and 1940 to 1980 saw a
  millionfold increase. The pace has been even quicker since then,
  a pace which would make humanlike robots possible before the
  middle of the next century. The vertical scale is logarithmic,
  the major divisions represent thousandfold increases in computer
  performance. Exponential growth would show as a straight line,
  the upward curve indicates faster than exponential growth, or,
  equivalently, an accelerating rate of innovation. The reduced
  spread of the data in the 1990s is probably the result of
  intensified competition: underperforming machines are more
  rapidly squeezed out. The numerical data for this power curve are
  presented in the appendix.
- Chip progress not only continued, it
  sped up. Shorter-wavelength light was substituted, a more precise
  way of implanting impurities was devised, voltages were reduced,
  better insulators, shielding designs, more efficient transistor
  designs, better heat sinks, denser pin patterns and
  non-radioactive packaging materials were found. Where there is
  sufficient financial incentive, there is a way. In fact,
  solutions had been waiting in research labs for years, barely
  noticed by the engineers in the field, who were perfecting
  established processes, and worrying in print as those ran out of
  steam. As the need became acute, enormous resources were
  redirected to draft laboratory possibilities into production
  realities.
- The wave-like nature of matter at very small scales is a problem
  for conventional transistors, which depend on the smooth flow of
  masses of electrons. But, it is a property exploited by a radical
  new class of components known as single-electron transistors and
  quantum dots, which work by the interference of electron waves.
  These new devices work better as they grow smaller. At the scale
  of today's circuits, the interference patterns are so fine that
  it takes only a little heat energy to bump electrons from crest
  to crest, scrambling their operation. Thus, these circuits have
  been demonstrated mostly at a few degrees above absolute zero.
  But, as the devices are reduced, the interference patterns widen,
  and it takes ever larger energy to disrupt them. Scaled to about
  0.01 micrometers, quantum interference switching works at room
  temperature.
- The big freeze. From
  1960 to 1990 the cost of computers used in AI research declined,
  as their numbers dilution absorbed computer-efficiency gains
  during the period, and the power available to individual AI
  programs remained almost unchanged at 1 MIPS, barely insect
  power. AI computer cost bottomed in 1990, and since then power
  has doubled yearly, to several hundred MIPS by 1998. The major
  visible exception is computer chess (shown by a progression of
  knights), whose prestige lured the resources of major computer
  companies and the talents of programmers and machine designers.
  Exceptions also exist in less public competitions, like petroleum
  exploration and intelligence gathering, whose high return on
  investment gave them regular access to the largest computers.
- Agony to ecstasy. In
  forty years, computer chess progressed from the lowest depth to
  the highest peak of human chess performance. It took a handful of
  good ideas, culled by trial and error from a larger number of
  possibilities, an accumulation of previously evaluated game
  openings and endings, good adjustment of position scores, and
  especially a ten-million-fold increase in the number of
  alternative move sequences the machines can explore. Note that
  chess machines reached world champion performance as their
  (specialized) processing power reached about 1/30 human, by our
  brain to computer measure. Since it is plausible that Garry
  Kasparov (but hardly anyone else) can apply his brainpower to the
  problems of chess with an efficiency of 1/30, the result supports
  that retina-based extrapolation. In coming decades, as
  general-purpose computer power grows beyond Deep Blue's
  specialized strength, machines will begin to match humans in more
  common skills.

Peek at 1998 Moravec book - Chapter 3. Power and Presence

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AI MIPS book computer memory robot trend

Power and Capacity
Information handling capacity in computers has been growing about ten
million times faster than it did in nervous systems during our
evolution. The power doubled every two years in the 1950s, 1960s and
1970s, doubled every 18 months in the 1980s, and is now doubling each
year.
3 more annotations...
- Computer power: 1900-1997
- AI Computer power: 1900-1997
- Progress in Computer Chess, a thin slice of AI

30 May 07

解決 memory leak in FreeBSD

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C FreeBSD leak memory

26 May 07

York Jong's Library tagged memory → View Popular

透視記憶

被灌水的 Python

《机器人》第三章－能力和显现

最快速度找到内存泄漏

Thinker: 資源管理與程式

When will computer hardware match the human brain? by Hans Moravec

Peek at 1998 Moravec book - Chapter 3. Power and Presence

Power and Capacity

Computer power: 1900-1997

AI Computer power: 1900-1997

Progress in Computer Chess, a thin slice of AI

解決 memory leak in FreeBSD

不用腦意識記憶也能學習？

探索 Linux Memory Model (上)

探索 Linux Memory Model (下)

記憶形成受到RNA干擾機制的調控

NAND vs. NOR flash

Memory-prediction framework

Coffee Break Brain

Study Reveals New Vaccine Reverses Memory Loss

Numenta, Inc.

Conscious And Unconscious Memory Linked In Storing New Information

Is Your Memory Erased While You Sleep?: Scientific American

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