This week I have left my steady 9 to 5 job as a graphic designer to become a recluse from society, pursue my MA final year project about embodying feelings into ANN.

I am still unsure how I feel about this decision.

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A Higgs-Boson enters church, the priest says “We don’t allow Higgs-Bosons in here”.
Higgs-Bosons says “But without me how can you have mass?”.

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There is a strong conviction in humans that flies are like machines. Those, and other insects, may experience faint feeling, qualias (…). How many neurones does a brain actually need to produce a consciousness? Ten-thousand, a million or a billion? We do not know this at the moment.

Christof Koch, The Quest for Consciousness: A Neurobiological Approach, chapter 11, translated by me

Humans display an autonoetic consciousness, which allows them to built an image of themselves, as well as form a link between their past and future actions. But consciousness itself is not bound with such requirements, we know that from cases of people with sever amnesia or Korsakoff’s syndrome. A good description of adaptation to living with Korsakoff’s syndrome can be found in chapter 2 of Oliver’s Sacks The Man Who Mistook His Wife for a Hat. There is no doubt that Jimmy, as well as other of Sacks’ patients, consciously experience the world around them. Report of their interest in arts, spiritual life leave no doubt that in their mind qualia still exist.

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Going beyond the idea of primary perception*, what do we know about the consciousness of plants?

We know plants have different kinds of memory: immune, term and transgenerational.

We know some of plant’s memory is based on epigenetics.

We know plants communicate with each other or different part of themselves in various ways.

What kind of consciousness, if any, do plants have? With their rich sensuous life, that is so different from ours. Did they develop a different kind of qualia, ones that we cannot even imagine?

Further watching:
Stefano Mancuso: The roots of plant intelligence
Prof. Ariel Novoplansky: Learning Plant Learning

Further reading:
Do Plants Think? Scientist Daniel Chamovitz unveils the surprising world of plants that see, feel, smell and remember

* the thesis popularized by Cleave Backster about plants being sentient and responding to human thoughts

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Artificial neural networks are shaped by their environment, which they get to know through the data. An ANN, unlike some programs, exists in relation to it’s surroundings.

We can influence our ANN by the choice of input.

This is similar to how living organisms are shaped by their surroundings.

What can ANN add to the ongoing discussion on nature versus nurture?

We can grant an ANN possibility to learn i.e. recognising MINST digits, through sufficient amount of neurons, correct learning algorithms and softmaxlayer. But an ANN without necessary data to learn on, it is incapable of doing the task.

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In social robotics exists a term neglect tolerance. It is a time that robot can operate without interaction with human. Robots with more autonomy seem to cope better with such loneliness.

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The question some ask is: why do we want to design autonomous robots?

Autonomy is one of key issues in designing social robots. Autonomy allows for  finding a creative and new solution to problems thus allowing for adaptation to the environment.

“… Dreyfus (1972) has suggested that computers would need to have bodies in order to gain the experience necessary to become truly creative, while others have suggested that human creativity is a social phenomenon and would be as impossible for isolated mind as it is present for an isolated computer.”

“Structure of Psychology; An Introductory Text”, (1981), C.I.Howarth, W.E.C. Gillham, ‘Theories of machines’ p.180

As we see today the gap is slowly closing, autonomy and creativity are necessary for a smooth interaction of machines and humans in most social scenarios.

Today, design of robots differs in the autonomy it gives them. Robots with no autonomy exist only in teleoperation or Wizard of Oz scenarios, where the man manipulates a mechanical skeleton.  This kind of autonomy may be useful but  renders robot useless without human presence.

On the LOA – level of autonomy – scale (as in works of Tom Sheridan) can be attributed several diffrent degrees of autonomy, from direct control of teleoperation to absolute autonomy in human-robot collaboration scenario.

The description of the scale:

1. Computer offers no assistance; human does it all.
2. Computer offers a complete set of action alternatives.
3. Computer narrows the selection down to a few choices.
4. Computer suggests a single action.
5. Computer executes that action if human approves.
6. Computer allows the human limited time to veto before automatic execution.
7. Computer executes automatically then necessarily informs the human.
8. Computer informs human after automatic execution only if human asks.
9. Computer informs human after automatic execution only if it decides too.
10. Computer decides everything and acts autonomously, ignoring the human.

As cited in Foundations and Trends in Human–Computer Interaction Vol. 1, No. 3 (2007) M. A. Goodrich and A. C. Schultz


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Two atoms were walking across a road when one of them said, “I think I lost an electron!”
“Really!” the other replied, “Are you sure?”
“Yes, I ‘m absolutely positive.”

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Artificial neural networks are based, as the name would suggest, on living neural networks. Neurons that build ANN are not exact models of living neurons, they are their idealized (simplified) vision.

Let us compare those two mechanism together.

In a living nervous system, such like the one humans have, nerves transfer signals by neurotransmitters that are activated by electrical stimuli. An artificial neuron is a less complicated structure, with an activation function inside that passes the signals forward.

Biological neurons

possesses a cell body (often called the soma), dendrites, and an axon. Dendrites are thin structures that arise from the cell body, often extending for hundreds of micrometres and branching multiple times, giving rise to a complex “dendritic tree”. An axon is a special cellular extension that arises from the cell body at a site called the axon hillock and travels for a distance, (…). The cell body of a neuron frequently gives rise to multiple dendrites, but never to more than one axon, although the axon may branch hundreds of times before it terminates. At the majority of synapses, signals are sent from the axon of one neuron to a dendrite of another.

From Wikipedia

Artificial neurons are connected to each other by weights. It’s the weights that are updated by learning algorithms, so the informations that are processed by artificial neurons shape the architecture of the network. Each neuron  receives inputs from the other neurons, the effect on each consecutive neuron is controlled by weights.

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 “… dream’s evanescence, the way in which, on awakening, our thoughts thrust it aside as something bizarre, and our reminiscences mutilating or rejecting it — all these and many other problems have for many hundred years demanded answers which up till now could never have been satisfactory.”

Sigmund Freud, The Interpretation of Dreams

Sigmoid Belief Nets are networks which form answers as beliefs (probabilities). For learning these complicated networks a wake-sleep algorithm was developed. In the awake part of the process informations are stored and weights learn. The problem which arises in Sigmoid Belief Nets is that

“it’s hard infer the posterior distribution over hidden configurations when given a datavector.”

(Neural Networks for Machine Learning, Coursera, Geoffrey Hinton and collaborators, lecture 13)

The need arises to un-learn some of the data structures from the awake process, in the stage named ‘sleep’.

This process may still lead to incorrect model averaging.

(Neural Networks for Machine Learning, Coursera, Geoffrey Hinton and collaborators, lecture 13)

When first implemented, wake-sleep algorithm was seen as a algorithm in which human brain worked. In this scenario, human brain would store data during the day, then while sleeping the brain would go through the information and ‘correct’ the wrongly defined connections. Is this the true nature of our dreaming?

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In psychology, Memory is the process by which information is encoded, stored, and retrieved. Encoding allows information that is from the outside world to reach our senses in the forms of chemical and physical stimuli. In this first stage we must change the information so that we may put the memory into the encoding process. Storage is the second memory stage or process. This entails that we maintain information over periods of time. Finally the third process is the retrieval of information that we have stored. We must locate it and return it to our consciousness. Some retrieval attempts may be effortless due to the type of information.

From Wikipedia

Memory process:

  • encoding information (forming a memory)
  • storing information (retaining the memory)
  • retrieving information (recalling memory)

The Autoencoders

The reconstruction of data from partial information is a very human thing. We do not need to remember every detail to find informations we stored useful. What we don’t remember our brain substitutes with generic data. The autoencoders, neural networks, use similar device. In the process of storage they use Principal components analysis, encoding data points in the direction of the most variance, allowing for more compact storage. In this storing process we loose information about remaining directions. In the recalling process Autoencoders reconstruct the data from partial information.

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Heisenberg went for a drive and got stopped by a traffic cop. The cop asked, “Do you know how fast you were going?” Heisenberg replied, “No, but I know where I am.”

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