Part1 (up to “spontaneous solutions”)

Task1. Match the words in column A to the words in column B

A                                                       B

1.  marvel                          wholly

2.  pupil                             that can be found or obtained

3.  intricate                          made, done, or happening without method or conscious decision

4.  entirely                        immediately; rapidly, so fast

5.  available                      to make an effort to understand or deal with (a problem)

6.  To get to grips            to give all your attention to something

7.  to anticipate                a small, round, black hole in the centre of your eye

8.  random                             to consider the first thing to be the same as the second thing.

9.  to take for granted,           to predict

10. to home in                     to accept or assume without question

11. in such short order     very complicated or detailed                       

Task2 Say whether the following is true, false or not mentioned.

1.  The mammalian eye is a perfect machine , which is a good example of divine intervention and doe not need any proof.

2.  Our knowledge of evolution is not enough to understand the way it works.

3.  All in all evolution processes make a complicated mechanism capable of learning by its mistakes to create better variants.

4.  Genetic mutations are the most important principle of evolution.

5.  Some pioneers in the field of population genetics ‘claimed’ that mutation on the gene is mainly caused by the natural selection pressure, and thus ‘nature selection’ is still the only cause for adaptation

6.  Involving learning in evolution is sure to be against one of the main principles of evolution as evolution cannot anticipate.

7.  Crossing a road has something in common with natural selection.

8.  Scientists think this similarity is only superficial: the knowledge of one does not amount to the knowledge of the other.

9.  Any claim that mutations can lead to Darwin-mechanism is not science.

10. Bayesian updating was created specially to model biological processes.

11. Since genes form a network ,changes in one gene may result in changes in the others in the network.

12. We were not expected to have over 25000 genes so when it was discovered we have fewer , it was taken for granted.

13. If evolution works in the same way our brain or neural networks do, it is quite understandable why it copes with problems so well and so fast.

Task3. Answer the following questions.

1.  What does the author mean by saying ‘And these are just the tip of the iceberg of evolution’s incredible prowess as a designer’?

2.  Why was Charles Darwin amazed?

3.  How is natural selection explained traditionally?

4.  Where does learning come in?

5.  What do evolution and a Bayesian algorithm have in common?

6.  Why do we have a relatively small number of genes, fewer than 25000 against the expected 100000?

7.  What conclusion can be drawn from Valiant’s and Chastain’s discoveries?

8.  How is a phenotype fit in a given environment created?

9.  What makes the process of learning tick?

10. Where can neural networks be used?

Part 2. ( from ‘Spontaneous solutions’ up to the end)

Task1. Find words/expressions meaning the following.

1.      to make smaller or less in amount, degree, or size; to, result in

2.           to lead

3.           to meet, to come across;

4.            being alike;

5.            to consider;

6.            to cause;

7         that cannot be avoided;

8.            causing or likely to cause an argument; controversial;

9.            to extract (information) with difficulty;

10.         understanding

Task2. Say if the following is true, false or not mentioned.

1.  In Watson’s model genes form different phenotypes depending on the model configuration ,which look on the display like a picture consisting of separate elements.

2.  When all mutations were allowed to survive the model started producing a combined image which bore resemblance both to Darwin and Hebb,

3.  Learning accelerates evolution as individuals cam acquire during their life necessary features for survival.

4.  Once the genotype has found the right solution, it can remember it and use in a different situation.

5.  The way the genome determines a crocodile’s sex depending on the temperature is a good example of the above learning.

6.  Remembering and reproducing past solutions is enough to imitate the work of the human brain.

7.  Each time nature creates a new organ it has to begin from the very beginning.

8.  Other scientists supported Watson’s work finding it new and interesting.

9.  The concept of evolvability has received wide recognition

10. Evolutionary algorithms can help us to understand the great progress evolution made in the last 3.8 billion years and help the computer to defeat a human player in the game of Go.

Task3. Answer the following questions.

1.  What did Watson’s model involve?

2.  What happened when the selection criterion was removed?

3.  Apart from remembering and reproducing past solutions what is a very important feature of learning systems?

4.  Why is it so important?

5.  What argument does Watson use to prove evolution is smart?

6.  What is behind the concept of involvability?

7.  Why is it controversial?

8.  How can fast transitions from creating the same molecules again and again to more complex structures be explained?

9.  What do the game of Go and the major evolutionary transitions have in common?

10. How does Watson’s hypothesis agree with Darwinian evolution? Can we say it is a sign of the divine intervention?