Monday, May 29, 2017

Generalists and specialists

As artificial intelligence develops to become even better at learning what humans can do, I think it is even more important for us humans to think about what we should do. What are the things that humans are good at, and what are the things that will eventually become things left to machines?

First, the success of AI in learning single tasks, and the greater difficulties with integrating things from a wider range of fields mean that humans will probably continue to have to be the generalists that binds together different fields of specializations to achieve an overall bigger objective. So we probably will continue to need managers with people management skills able to bring together a team of people with different talents to work on a project.

But how about specialists, people who are good at doing certain things? Well, the recent success of deep learning has shown that machines can now be trained to do certain things much much better than humans. That's because people get better at these things by doing them, and machines are able to devote a lot more time to doing this things repetitively to become better at them than humans. So machines are likely to replace cucumber sorters, since they can learn to do this offline (by training on pictures of cucumbers, which requires a lot less time than actually finding cucumbers to look at and sort), and once trained, they can be put to the actual sorting task for hours and hours without having to worry about labor laws about working hours. Compared to humans, they take less time to train, and can work for much longer hours.

However, we will probably still need specialists in which ability is not just gained by repetitive learning, but also requires some form of creativity. Although there is on-going work to teach machines creativity, based on deep learning techniques, these are still limited to training using a big dataset to try and generate something from it. It is evolutionary, not revolutionary. For that revolutionary break, we still need human creativity, and that is where humans will continue to find a niche for them to specialize in.

In short, as computer become better and better at mimicking humans, through advances in AI, humans will need to find a place for themselves. These could be as generalists, where AI is still unable to adequately mimic, or as specialists in fields requiring human creativity.

Wednesday, May 17, 2017

Two types of wargames

Recently, I have been reading a book about Japan's defeat in WWII. It led me to think about wargames in general.

To me, there are two types of wargames.

One is the confidence building type. The training type. Where the enemy behaves in much the way that current intelligence assesses he may behave. It is about executing a plan against a known enemy, to test the effectiveness of the plan, build confidence in the plan, and train people in how to carry out that plan.

It is the "winning" type of wargame. Where the good guys (the friendly side) is supposed to win. The enemy behaves like how we think he will behave (which may be close, or way off, depending on how good our intelligence and assessments are).

The other type of wargame is the "losing" type. This is when the enemy does what he can do, given the resources available to him. The difference is between can and may. "May" is about probability, about what he is likely to do. "Can" is about possibility, no matter how small, and is about what he is able to do. Here, the goal is to try to beat the good guys, to make the friendly side lose. This is to expose the weaknesses in existing plans, to expose areas which have not been considered in planning or were not answered. Here, consideration must be given beyond the military to bring in economic and politic aspects of war, since war is not a standalone condition and is part of a bigger grand strategy.

It is about identifying risks, military or otherwise, so that they can be dealt with.

But nobody likes to lose. Nobody likes to be shown his weaknesses. And it can be bad for morale too. So this type of wargame is rarely conducted. Yet it is just as important as the "winning" type. Only with both will you be able to be fully prepared for this dangerous feat known as war.

So I think what is important is that in a wargame, we must not be afraid of losing. Because losing helps to make the plan better. And it is better to lose in a wargame so that we can improve the plan, rather than lose in an actual war, by which time we won't have the luxury of time to improve the plan.

Wednesday, May 10, 2017

Facebook's grammar is...

Just when Facebook announced this new phase in their machine translation research...

A novel approach to neural machine translation

This came up in my Facebook feed.






"Rain is forecast."

Somehow, it doesn't seem right to me. I would think it should be "Rain is in the forecast" or "Forecast is rain"... I mean, we say "sky is blue" but not "blue is sky", right?

Then it got me thinking. Maybe Facebook got their forecast for Yokohama from a Japanese source (which only makes sense, right?). Which means it was in Japanese. Which means it was then translated automatically by Facebook's machine translation software into English to put this on my feed. Which means this shows the current state of Facebook's state-of-the-art machine translation for Japanese to English.

I guess I still have time to earn a living as a translator.

Monday, May 08, 2017

We need fats to give birth to the next generation

A thought that came to mind a while ago.

Humans and companies are quite alike in a certain way. Both need fats in order to be able to give birth to the next generation.

For humans, females need a certain level of body fat in order to have normal reproductive functions. When there is not enough fat, reproductive functions cease, which means they cannot give birth to the next generation.

Similarly, companies need fats (not the oily kind, but as in having some form of abundance or excess of something) so that they can devote part of their resources towards training and education to nurture their next generation of people. If companies are so caught up in the day-to-day work that they have no time or money to put into nurturing their people, they will not be able to sustain their operations.

While this is a simple analogy, I think it shows how important having some form of abundance is. This even shows itself in artificial societies. In the Sugarscape model, agents need to gather a certain amount of resources before they can "reproduce". This is the "excess/abundance" that we see in the real world, whether biological or financial. A good reminder for myself in any future research into artificial societies.

Thursday, May 04, 2017

Star Wars Day - 40th anniversary

Today is Star Wars Day!

This year marks the 40th anniversary of Star Wars. Wow! 40 years!

May the Force be with you!

#Maytheforcebewithyou #StarWarsDay