【推荐1】Although literacy appeared independently in several parts of the prehistoric world, the earliest evidence of writing is the cuneiform Sumerian script on the clay tablets of ancient Mesopotamia, which, archaeological detective work has revealed, had its origins in the accounting practices of commercial activity. Researchers demonstrated that preliterate people, to keep track of the goods they produced and exchanged, created a system of accounting using clay tokens as symbolic representations of their products. Over many thousands of years, the symbols evolved through several stages of abstraction until they became wedge-shaped (cuneiform) signs on clay tablets, recognizable as writing.

The original tokens were three-dimensional solid shapes — tiny spheres, cones, disks, and cylinders. A debt of six units of grain and eight head of livestock, for example, might have been represented by six conical and eight cylindrical tokens. To keep batches of tokens together, an innovation was introduced whereby they were sealed inside clay envelopes that could be broken open and counted when it came time for a debt to be repaid. But because the contents of the envelopes could easily be forgotten, two-dimensional representations of the three-dimensional tokens were impressed into the surface of the envelopes before they were sealed. Eventually, having two sets of equivalent symbols — the internal tokens and external markings — came to seem redundant, so the tokens were eliminated, and only solid clay tablets with two-dimensional symbols were retained. Over time, the symbols became more numerous, varied, and abstract and came to represent more than trade commodities, evolving eventually into cuneiform writing.

The evolution of the symbolism is reflected in the archaeological record first of all by the increasing complexity of the tokens themselves. The earliest tokens, dating from about 10,000 to 6,000 years ago, were of only the simplest geometric shapes. But about 3500 B.C.E., more complex tokens came into common usage, including many naturalistic forms shaped like miniature tools, furniture, fruit, and humans.

【推荐1】Machine learning sees reasoning as a categorization task with a fixed set of predetermined labels. It views the world as a fixed space of possibilities, calculating and weighing them all. This approach, of course, has achieved notable successes when applied to stable and well-defined situations such as chess or computer games. When such conditions are absent, however, machines struggle.

In 2008, Google launched Flu Trends, a web service that aimed to predict flu-related doctor visits using big data. The project, however, failed to predict the 2009 HINI flu outbreak. After several unsuccessful adjustments to its algorithm (算法), Google finally stopped the project in2015.

In such unstable situations, the human brain behaves differently. Sometimes, it simply forgets. Instead of getting trapped in irrelevant data, it relies only on the most recent information and makes creative decisions. This is a feature called intelligent forgetting. Adopting this approach, an algorithm that relies on a single data point would have reduced Google Flu Trends' prediction error by half.

Intelligent forgetting is just one dimension of psychological AI, an approach to machine intelligence that also includes other features of human intelligence such as causal reasoning, intuitive (直觉) psychology, and physics. Soon, this approach to Al will finally be recognized as fundamental for solving poorly-defined problems. Exploring these amazing features of the human brain will finally allow us to make machine learning smart.

One feature of psychological Al is that it is explainable. Until recently, researchers assumed that the more transparent an AI system was, the less accurate its predictions were. This mirrored the widespread but incorrect belief that complex problems always need complex solutions. Now, this idea will be laid to rest. As the case of flu predictions illustrates, powerful and simple psychological algorithms can often give more accurate predictions than complex algorithms. Psychological AI opens up a new vision for explainable AI: Instead of trying to explain complex systems, we can check first if psychological Al offers a simple and equally accurate solution.

Without the help of human psychology, it will become clearer that the application of this type of machine learning to unstable situations eventually runs up against impassable limitations. We will finally recognize that more computing power makes machines faster. Not smarter.

【推荐2】Some women say the voice in their head gets kinder as they enter midlife, while other women become more critical of themselves. The difference in views appears to be related to the ability to accept age-related changes to their body, researchers have found.

In 2013, Professor Paxton, of the Universityof La Trobe’s Department of Psychology, was interviewed on ABC Melbourne about body image and well-being in women entering midlife. The ABC posted on their Facebook page the topic of the program and the question: Does the voice in your head get kinder as you get older?

The study found 56 percent of comments suggested the voice in women’s heads did get somewhat kinder with age. This view was associated with women accepting the age-related changes in their body as normal. “If they gained weight or they got wrinkles, it was a normal part of aging and they felt fine about it and it didn’t disturb their sense of self,” Professor Paxton said, “They were more likely to value health and well-being as opposed to appearance.”

On the other hand, 40 percent of comments suggested the voice did not get any kinder with age. This view was associated with women finding it difficult to accept being so different from the youthful ideal of women that is upheld in advertising and in the media. Women who expressed this view were concerned about how “invisible” they had become as a consequence. “They reacted negatively to the changes in their bodies as they aged and were very much aware that in our culture, youthful bodies are idealized,” Professor Paxton said.

Understanding why some women are preoccupied with body image in midlife more than others is important given that having a bad body image can lead to physical and mental health problems, Professor Paxton said. This can include self-consciousness and anxiety, eating disorders, and an unwillingness to show oneself in public, which can reduce the amount women exercise or socialize. “They become unsociable and don’t fulfill their full potential. Those sorts of things can make a person very unhappy.”

She said the insights from studies like this support the use of cognitive (认知的) behavioral therapy (治疗) designed to help women in midlife who have a preoccupation with body image to change negative thought patterns. This would involve discouraging them from comparing themselves to younger women, and encouraging acceptance of bodily changes related to age, and “self care” behaviors that improve health and well-being.