The Ten Most Ferocious Killers – Your Ego is One of Them

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Your ego is a strange thing. It can give you the strength to carry on in difficult circumstances but more often than not it can lead you astray and make you overreact. One of the worst forms of overreaction is when you inflict harm on your body, in a situation of disappointment and despair, which happens more often than one would think. I looked up the suicide rates and found the following numbers, as published by the American Foundation for Suicide Prevention:

Suicide is the 10th leading cause of death in the US. Each year, more than 40,000 Americans die by suicide. The annual suicide rate is 12.93 per 100,000 individuals, and on average, more than 100 suicides happen per day. These are quite impressive numbers. I don’t think that all these suicides can be ascribed to people in physical pain due to a disease or other circumstances for which these people see no other escape than suicide. I bet many people commit suicides in situations that many outsiders would perceive as a temporary freak of nature, and not as a reason to commit suicide if there can be any such reason at all. For example, the young boy who is desperate because the love of his life shows no interest in him, and even worse, criticizes him and falls in love with another boy. His ego is hurt, but few will accept this as a suicide reason and more of a life lesson that the boy needs to learn in the process of becoming a man. Another classic suicide example is the stockbroker jumping out of the window of a high-rise bank building after the collapse of the stock market. Do you think a poor beggar would show any sign of understanding when the broker’s body thuds onto the pavement in front of him? The reason the broker committed suicide, namely the fact that he has just been delegated from the class of the Haves to the class of the Have Nots, is not something the beggar needs to be afraid of here. In fact, belonging to the class of Have Nots makes up the very existence of the beggar so why should he be afraid of it. He may not like it, but he can take comfort in the fact that millions are sharing the same fate with him.

Experts will argue that these examples are over-simplified because, and I’m copying here again from the American Foundation for Suicide Prevention:

There’s no single cause for suicide. Suicide most often occurs when stressors exceed current coping abilities of someone suffering from a mental health condition. Depression is the most common condition associated with suicide, and it is often undiagnosed or untreated. Conditions like depression, anxiety and substance problems, especially when unaddressed, increase risk for suicide.

Nevertheless, whether one sees suicide as a multi-cause disease or not, I think the overall conclusion is the same. Our ego leads us astray in a situation that would not justify this action by any means in the opinion of a neutral observer. The anxiety of not getting what you want or losing something can let the ego initiate a self-destructive process with suicide as the possible final climax. We need to be aware of this force that our ego exerts on us and keep it at bay, as much as we can, by constantly re-evaluating our goals and learning to let go before self-destruction begins.

For the curious reader who has made it this far, here are the nine leading reasons of death in the United States, according to the Centers for Disease Control and Prevention, with intentional self-harm on 10th place:

  1. Heart disease: 611,105
  2. Cancer: 584,881
  3. Chronic lower respiratory diseases: 149,205
  4. Accidents (unintentional injuries): 130,557
  5. Stroke (cerebrovascular diseases): 128,978
  6. Alzheimer’s disease: 84,767
  7. Diabetes: 75,578
  8. Influenza and Pneumonia: 56,979
  9. Nephritis, nephrotic syndrome, and nephrosis: 47,112
  10. Intentional self-harm (suicide): 41,149

Fighting Malaria with Image Analysis and Machine Learning

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For readers interested in image analysis and machine learning, here is the video stream of my presentation to the Board of Regents at the U.S. National Library of Medicine, National Institutes of Health. I presented the status of our automatic cell counting tool for malaria diagnosis, which we are developing with funding from HHS Ventures.

 

 

One Giant Leap for AI, One Small Step for Man

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The deed is done. Another intellectual stronghold of humans has gone the way of the dodo. Just last night, Google’s computer program called AlphaGo has won a match against one of the world’s leading Go players.

Less than twenty years after IBM accomplished a similar feat in chess by beating the world champion in a now famous match, with the Deep Blue chess computer. Around that time, I had just finished my Ph.D. and was looking for a job in artificial intelligence (AI). Naturally, IBM was an obvious candidate. I was an active chess player myself when the Deep Blue match took place, and like most players, I was not particularly happy seeing a piece of silicon taking away the grace of chess. I felt especially insulted by the way Deep Blue had won the match. To me, it all came down to a brute-force search for the best move among all possible move sequences, an approach that one would not associate with intelligence at all. I had a discussion with one of the IBM managers who downplayed this brute-force aspect of the problem, insisting that their software had shown intelligent behavior by beating an accomplished human being in an apparently rational activity. While I did not join IBM, I think everybody has to admit that Deep Blue was a major accomplishment.

The game of Go has been more resilient and human players have kept the upper hand for longer. Some say this is because of the larger number of possible moves, the so-called branching factor, which increases the number of potential move sequences to an astronomically high number that is intractable even for modern computers. Others say that the longer dominance of humans in Go was simply because less time and resources have been devoted to conquering Go. Be that as it may, as a matter of fact, AlphaGo works differently than Deep Blue. While it still relies on a tree search component, it also applies a neural network to find candidate moves. The neural network reduces the number of choices in a given position by pre-selecting the best candidate moves, which sets AlphaGo apart from a brute-force exhaustive search. While Deep Blue most certainly used many rules and heuristics to cut down on the number of candidate moves, the use of a neural network strikes me as more powerful. The network that AlphaGo uses has been trained on hundreds of thousands of Go games played between world-class players. Besides, AlphaGo is learning from games that it is regularly playing against itself. So yes, progress has been made, and the latest victim is Lee Sedol, the professional Go player who succumbed to AlphaGo last night.

The artificial intelligence community is now hoping that the approach followed by AlphaGo can be applied to other areas so that this new form of artificial intelligence can help humans instead of just trying to beat them. Deep Blue was very much tailor-made for beating the world chess champion, but outside the chess domain, there was little it could do. Time will tell if AlphaGo will lead us to new horizons, or if it will share the same fate as Deep Blue, which was dismantled shortly after its game-changing match with the world chess champion.

 

Making Decisions with Intuitive Confidence

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Have you ever wondered how you make your decisions, and how the decision-making process actually happens in your mind? Let’s say, you are facing a problem with two potential solutions A and B. What happens in your brain when you prefer one solution over another. For example, if you are losing confidence in Solution A and you are gaining more and more confidence in Solution B at the same time, until you are convinced that Solution B will solve your problem. How does this process happen? Is it a linear process? Are you gaining confidence in Solution B to the same extent as you are losing confidence in Solution A? From the following paper I just published one can argue that this is not the case:

 

Detecting Disease in Radiographs with Intuitive Confidence
Stefan Jaeger, “Detecting Disease in Radiographs with Intuitive Confidence,” The Scientific World Journal, vol. 2015, Article ID 946793, 9 pages, 2015. doi:10.1155/2015/946793

 

In fact, from the paper one can conclude that changing one’s mind from A to B happens in three phases. In the first phase, one quickly gains confidence in B while only slowly losing confidence in A. During the second phase, confidence in A is lost to the same extent as confidence is gained in B. Finally, in the third phase, the remaining confidence in A is quickly lost while only a small confidence in B is still being gained. The paper shows that these three phases can be explained mathematically by following a sector on a circle.

 

Now, what implications does this have for everyday decision making? Well, the three phases are the natural, or intuitive way of building up confidence in a decision. Any communication, either between humans or between humans and machines, violating this process will cause communication problems and stress. For example, in order to switch your standpoint, patience may be needed, as the confidence in established approaches or opinions only slowly degrades in favor of new and better solutions. On the other hand, once a certain confidence level is reached, changing opinions or accepting new solutions can happen very quickly. This conclusion may not sound as new information to some, but having a mathematical model for this process allows a scientific approach.The paper also provides an example of how a machine can output proper confidence, meaning intuitive for humans, in its automatic detection of lung disease for computer-added diagnostics.