The whale families we work with, members of the Eastern Caribbean Clan, are shrinking. Their population is declining by as much as 4 percent a year largely a result of climate change and the increasing human presence in these waters. We are not just losing specific whales that we have come to know as individuals; we are losing a way of life, a culture — the accumulated wisdom of generations on how to survive in the deep waters of the Caribbean Sea. They may have lived here for longer than we have walked upright.
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Every culture, whale or otherwise, is its own solution to the problems of the environment in which it lives. With its extirpation, we lose the traditional knowledge of what it means to be a Caribbean whale and how to exploit the deep sea riches around the islands efficiently. And that cannot be recovered, not even if the global population of sperm whales was robust enough to support remigration into the Caribbean. These would be different whales, from elsewhere, who do things differently. This region would be profoundly impoverished for the new whales, who would be more vulnerable here. The species as a whole would lose some of its repertoire on how to survive.
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Species conservation should not be just about numbers. The definition of biodiversity needs to include cultural diversity.

• In physics, experiment and observation seem to be the basis for knowledge. The physicist might want to construct a hypothesis to explain observations that do not fit current thinking and devises and performs experiments to test this hypothesis. Results are then collected and analysed and, if necessary, the hypothesis modified to accommodate them.
• In history there is no experimentation. Instead, documentary evidence provides the historian with the raw material for interpreting and understanding the recorded past of humanity. By studying these sources carefully a picture of a past event can be built up along with ideas about what factors might have caused it.
• In a literature class students set about understanding and interpreting a text. No observation of the outside world is necessary, but there is a hope that the text can shed some light upon deep questions about what it is to be human in a variety of world situations or can act as a critique of the way in which we organize our societies.
• Economics, by contrast, considers the question of how human societies allocate scarce resources. This is done by building elaborate mathematical models based upon a mixture of reasoning and empirical observation of relevant economic factors.
• In the islands of Micronesia, a steersman successfully navigates between two islands 1,600 km apart without a map or a compass.

Epistemology is the study of knowledge. Epistemologists concern themselves with a number of tasks, which we might sort into two categories.
First, we must determine the nature of knowledge; that is, what does it mean to say that someone knows, or fails to know, something? This is a matter of understanding what knowledge is, and how to distinguish between cases in which someone knows something and cases in which someone does not know something. While there is some general agreement about some aspects of this issue, we shall see that this question is much more difficult than one might imagine.
Second, we must determine the extent of human knowledge; that is, how much do we, or can we, know? How can we use our reason, our senses, the testimony of others, and other resources to acquire knowledge? Are there limits to what we can know? For instance, are some things unknowable? Is it possible that we do not know nearly as much as we think we do? Should we have a legitimate worry about skepticism, the view that we do not or cannot know anything at all?

The idea of the knowledge economy is appealing. The only problem is it is largely a myth. Developed western economies such as the UK and the US are not brimming with jobs that require degree-level qualifications. For every job as a skilled computer programmer, there are three jobs flipping burgers. The fastest-growing jobs are low-skilled repetitive ones in the service sector. One-third of the US labour market is made up of three types of work: office and administrative support, sales and food preparation.

You may have heard people say that you do not have to cite your source when the information you include is “common knowledge.” But what is common knowledge?
Broadly speaking, common knowledge refers to information that the average, educated reader would accept as reliable without having to look it up. This includes:

• Information that most people know, such as that water freezes at 32 degrees Fahrenheit or that Barack Obama was the first American of mixed race to be elected president.
• Information shared by a cultural or national group, such as the names of famous heroes or events in the nation’s history that are remembered and celebrated.
• Knowledge shared by members of a certain field, such as the fact that the necessary condition for diffraction of radiation of wavelength from a crystalline solid is given by Bragg’s law.
• However, what may be common knowledge in one culture, nation, academic discipline or peer group may not be common knowledge in another.

人間を他の動物と分け隔てる大きな特徴は、見たり聞いたりしていることをそのまま鵜呑みにして行動するのではなく、いろいろな概念やことばによって意味をつかむことができる点にある。また、多くの経験を知識に変え、その知識を実践に応用することによって、さらにしっかりした知識を創り出していくことができる点にある。

Automobile technicians are in demand to repair the software that now powers our cars; manufacturing technicians, to upgrade the numerically controlled machines and 3-D printers that have replaced assembly lines; laboratory technicians, to install and test complex equipment for measuring results; telecommunications technicians, to install, upgrade, and repair the digital systems linking us to one another.
Technology is changing so fast that knowledge about specifics can quickly become obsolete. That’s why so much of what technicians learn is on the job.

機械的に覚えたものを記憶と呼びます。この記憶がいくら増えても実は知識にはなっていきません。ここが最大の問題点なのです。
知識とは、意味理解の伴った記憶のことであり、記憶そのものとは区別しなければなりません。「牴牾…辻褄が合わないこと」を幼児が覚えたとして、例えば、牴牾って何と聞いたとします。すると間髪入れずに辻褄が合わないことと答えます。親御さんは小躍りして喜ぶでしょう。次に辻褄って何と聞きます。？わかんなーい。となります。これが単なる記憶であり、知識では無いのです。
こういう勉強ばかりをしていると、算数も低学年まではいいのですが、高学年になって、苦労をし始めます。

第1章 記憶の現象学
　A. さまざまな記憶
　B. 陳述できる記憶
　C. 行動化される記憶（陳述できない記憶）
第2章 生活記憶の障害
　A. 記憶障害のキーワード
　B. 生活記憶の障害
　C. 前頭葉損傷と健忘
第3章 意味記憶の障害
　A. 意味記憶の特徴
　B. 単語の意味記憶の障害
　C. 図像の意味の理解障害
　D. 実物の意味記憶の障害
　E. 意味カテゴリーと大脳損傷
第4章 手続き記憶の障害
　A. 視覚運動性手続き記憶の障害
　B. 知覚性手続き記憶の障害
　C. 認知性手続き記憶の障害
　D. 古典的条件反射
第5章 生活記憶の神経解剖学的構造
　A. 健忘の責任病巣
　B. 記憶のネットワーク
第6章 記憶の心理構造，統合的理解へ向けて
　A. 記憶の流れ
　B. 記憶と生活

Computers have long been compared to artificial brains, but now IBM has followed the comparison and built a working artificial neuron. The tech giant’s research center in Zurich created 500 of them to simulate a signal transfer similar to how the process works in an organic brain.

To achieve the singularity, it isn’t enough to just run today’s software faster. We would also need to build smarter and more capable software programs. Creating this kind of advanced software requires a prior scientific understanding of the foundations of human cognition, and we are just scraping the surface of this.
This prior need to understand the basic science of cognition is where the “singularity is near” arguments fail to persuade us. It is true that computer hardware technology can develop amazingly quickly once we have a solid scientific framework and adequate economic incentives. However, creating the software for a real singularity-level computer intelligence will require fundamental scientific progress beyond where we are today.

Theorists have proposed, and researchers have reported, that intelligence is a set of relatively stable abilities, which change only slowly over time. Although intelligence can be seen as a potential, it does not appear to be an inherent fixed or unalterable characteristic. … Contemporary psychologists and other scientists hold that intelligence results from a complex interaction of environmental and genetic influences. Despite more than one hundred years of research, this interaction remains poorly understood and detailed. Finally, intelligence is neither purely biological nor purely social in its origins. Some authors have suggested that intelligence is whatever intelligence tests measure.

The technological singularity (also, simply, the singularity) is the hypothesis that the invention of artificial superintelligence will abruptly trigger runaway technological growth, resulting in unfathomable changes to human civilization. According to this hypothesis, an upgradable intelligent agent (such as a computer running software-based artificial general intelligence) would enter a ‘runaway reaction’ of self-improvement cycles, with each new and more intelligent generation appearing more and more rapidly, causing an intelligence explosion and resulting in a powerful superintelligence that would, qualitatively, far surpass all human intelligence. Science fiction author Vernor Vinge said in his essay The Coming Technological Singularity that this would signal the end of the human era, as the new superintelligence would continue to upgrade itself and would advance technologically at an incomprehensible rate.

Artificial intelligence (AI) research has explored a variety of problems and approaches since its inception, but for the last 20 years or so has been focused on the problems surrounding the construction of intelligent agents – systems that perceive and act in some environment. In this context, “intelligence” is related to statistical and economic notions of rationality – colloquially, the ability to make good decisions, plans, or inferences. The adoption of probabilistic and decision-theoretic representations and statistical learning methods has led to a large degree of integration and cross-fertilization among AI, machine learning, statistics, control theory, neuroscience, and other fields. The establishment of shared theoretical frameworks, combined with the availability of data and processing power, has yielded remarkable successes in various component tasks such as speech recognition, image classification, autonomous vehicles, machine translation, legged locomotion, and question-answering systems.

Have you heard the story of the architect from Shiraz who designed the world’s most beautiful mosque? No one had ever conjured up such a design. It was breathtakingly daring yet well-proportioned, divinely sophisticated, yet radiating a distinctly human warmth. Those who saw the plans were awe-struck.
Famous builders begged the architect to allow them to erect the mosque; wealthy people came from afar to buy the plans; thieves devised schemes to steal them; powerful rulers considered taking them by force. Yet the architect locked himself in his study, and after staring at the plans for three days and three nights, burned them all.
The architect couldn’t stand the thought that the realized building would have been subject to the forces of degradation and decay, eventual collapse or destruction by barbarian hordes. During those days and nights in his study he saw his creation profaned and reduced to dust, and was terribly unsettled by the sight. Better that it remain perfect. Better that it was never built.

• 自然界にあるものに一切の境はない。境目というのは、分類し、理解をするために人間が勝手に定めたもの。所詮は、人間が自分たちのために作った分類に過ぎない。
• 植物は、動物から身を守るために毒を持つようになった。被子植物はアルカロイドという毒成分を身につけ、恐竜は対応できず中毒死し、衰退していった。
• 私たちの体の細胞には、自ら死ぬためのプログラムが組み込まれている。「死」は地球上に生まれた生命が創りだした発明品である。
• 人間は、さまざまに植物を改良して、変化させてきた。植物にとっては、人間の欲望に合わせて変化することは、自然界を生き抜く苦労に比べれば、何でもなかった。
• 植物プランクトンが作りだした酸素は、紫外線に当たってオゾンとなった。オゾンは上空に充満し、紫外線を遮り、海の中にいた植物が陸に進出するのを助けた。
• 人間は二酸化炭素を増やし、オゾン層を破壊し、植物を減らし、またもとの地球を取り戻そうとしている。その環境でいくらかの生物は進化を遂げるだろう。しかし人類は生き残れない。

「知性」とは、単に何かを理解したり、分析したりする能力ではなくて、それを自分に適用する「ふりかえり」の作業を含む、ということだろう。知性はその能力を行為者、つまり、人間という人格や自我の存在を示唆する。知能が高くても、知性が低い人はいる。それは知的能力は高いが、その能力が自分という存在のあり方へと振り向けられない人のことである。だから、犯罪者には「知能犯」はいるが「知性犯」はいないのである。

知性に必要な要素である「創造力」を持つのは人間だけではない。イルカはオリジナルの技を考え出し、ミズダコは人間が作った仕掛けを利用して生きている。また、コガラの実験では、先祖が暮らした場所が過酷な環境であるほど創造力にあふれるＤＮＡが受け継がれ、難問を解決する能力が発達していることが証明される。
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動物は危険回避のため、あるいはテリトリーを守るため、そして子どもに生きる術を教えるために、鳴き声やボディーランゲージを使ってコミュニケ―ションをとる。人間との関わりの中では、人間の言葉を理解し、時には「会話」に近いこともできるようになる。また他者への愛情や共感、協力、自己認識など高度な社会性を伴うコミュニケーション能力を持つ動物もいる。実験・観察を通して、驚くべき行動をするさまざまな動物の生態をわかりやすく解説。人間と動物の能力にそれほど差はなく、動物たちの能力には未知の領域があることがわかる。

We are not alone in our ability to invent or plan or to contemplate ourselves—or even to plot and lie.
Deceptive acts require a complicated form of thinking, since you must be able to attribute intentions to the other person and predict that person’s behavior. One school of thought argues that human intelligence evolved partly because of the pressure of living in a complex society of calculating beings. Chimpanzees, orangutans, gorillas, and bonobos share this capacity with us. In the wild, primatologists have seen apes hide food from the alpha male or have sex behind his back.
Birds, too, can cheat. Laboratory studies show that western scrub jays can know another bird’s intentions and act on that knowledge. A jay that has stolen food itself, for example, knows that if another jay watches it hide a nut, there’s a chance the nut will be stolen. So the first jay will return to move the nut when the other jay is gone.

When I say that one must start by thinking about all the things that might count as knowledge, I do not mean to claim that anything like classical epistemology is being pursued. First, for the sociologist of knowledge, or the Wittgensteinian philosopher, there is no classical epistemology—knowledge cannot be found in the absence of the activities of humans. The point is that we must start with an attempt to think about knowledge in a way that goes beyond human experience if we are to understand that experience properly. The starting point is to think of knowledge as “stuff” that might also be found in animals, trees, and sieves and then try to work out from this starting point what it is that humans have. Human experience alone is too blunt an instrument for the task.