Sure. But the Turing test is a piece of garbage too. I have a deep respect for Allan Turing, and all that he has done for science. But the Turing test was death to AI the moment he proposed it. It MUST be forgotten and burried, and maybe incidents like these can help us achieve that!

Nooo! That's the whole point of Do Androids Dream of Electric Sheep?/Bladerunner! If they put in Harrison, they must magically make him the same age! Then add that there's drugs that does that, so there's still two explanations; human or replicant. I mean, how can Decard have his inner conflict, if he knows he is human?

Or maybe because the Greenlandish people have their life disrupted from a deteriorating environment. Of course you Americans probably don't know them. (Albeit you've already caused them great harm with your military bases and forced relocations) People live in places where there's supposed to be ice. The melting disrupts fishing and hunting which is part of their culture and identity.

They could of course just become true westerners and leave their homes. But it's a country that already have enormous rates of unemployment, alcoholism and suicide. That's expected when their way of life is becoming increasingly impossible to live. There's not that much else they can do on that Island. Of course now that they've found oil, that maybe change. Soon they maybe even become as polluting as us! Because that's progress; let's not commune with nature, let's destroy it and drain the land of its resources. I don't blame them however; they did not destroy nature, we did, and they just have to follow suit to survive.

I have the same goal, along with many different dreams that range between the feasible and impossible. Dreaming big has brought me many different places as I pursue them. I've designed them such however that they are just out of reach, and in the case I should grasp one, I devalue it and think of others. That way I keep my momentum. Learning new knowledge is compulsive.

Problem is that I often cannot sleep, because my mind keep spinning. And I'm terrified of death, in that I have not yet accomplished anything I deem important.

Who says we should be content life? I am not. I am furious at it. Death is not welcome, and I shall die clinging to existence until my neural activity ceases.

Ah, it's time to update mine then. Bought it six year ago :)

Oh, and why it is interesting to pursue AI that can play an unknown game; procedural generated games. You can evolve those, and evaluate them based on how the AI agents play the generated games. If you can make an AI that plays somewhat like a human, you can then in turn procedurally generate games that humans can play. Think of starcraft for example; you could perfectly balance the three races and each unit, through these means. Even adjust the general rules of the game, such as economy and what not. It might show that a third resource is necessary to result in balance. Who knows?

I agree in regards to game AI; I said that there was a difference between research and the industry. In the industry it's just finite automata. In research it is neural networks, evolutionary algorithms, reinforcement learning, learning classifier systems, monte carlo techniques et cetera. My own master thesis is based on such AI. The reading I did on the field, also shows that others are researching in AI with these methods.

So yes, we agree. Game AI in research; machine learning and other complex techniques, Game AI in the industry; finite automata and other knowledge based techniques. However, since 2000, research has moved towards video games rather than board games; although research continue on board games of course. Thus the classic game AI, and modern game AI. Modern game AI is game AI applied to video games. For example, the video game I worked on, have its rules procedurally generated, and my AI agents must learn to play it through machine learning; that is a more difficult problem than Go or Chess. It makes sense that game AI should pursue solutions to harder games than Chess and Go, because not only is the challenge bigger, but it also have a potential use in the industry; researchers hope that at some point, the industry will begin using these techniques. It's still not the case however :(

It is also a great service in terms of knowledge sharing. I watched a lot of youtube videos from Stanford and others, while studying CS and AI. It's great for helping with reading up, as I can't remember everything that happened at my own lectures. Also some professors explain some things better than others; if confused about something in your lecture, look it up from other sources, and clear it up. In that sense I think it benefits CS study in general. If someone outside of college can learn it in this form, I do not know. Personally I doubt it. The ability to ask questions and to meet with other students and TA's, helps clear up a lot of the confusion that might arise. And I did confused a lot of times, until someone had a golden nugget to share that put in the missing piece. It's the same with reading a book; reading it doesn't mean understanding it. Sometimes you need outside help.

Oh, the container stowage problem is important economically and environmentally, because it shortens the time a container ship has to stay in harbor. Their research is obviously funded by that industry. Its product is simply a list of instructions to the crane operators, that results in both the fast unloading of containers, but also optimal placement; those two compliment each other.

Added it in with my thoughts of the book, because they based it on some of the theory of the book. Almost same algorithm, but obviously tweaked. Was part of the lecture, and was just nice to see a real example of the theory in the book being research on, and possible being utilized in the future by the industry ;)

The field of AI is no longer focused on creating humans brains as far as I've learned from my studies. They did dream big back then when the field first came to be, but the complexity of the problem became apparent. It's simply, currently, not possible.

There is planning, search and logic AI, which finds the best possible plans for different problems, and is often used in manufacturing. Such as designing computer chips, or for instructions to robots or cranes that builds, sorts or package. AI is capable of approximating solutions to problems that cannot be done through algorithmic means; as such AI often deal with problems in NP.

Another field is game AI, which I know most about. There's a plethora of sub-fields here. The traditional game AI dealt with solving games, and has influenced many games such as chess. (AI hasn't solved chess, but found many end games that humans did not know, and found solutions to end games that humans have theorized about for over a hundred years) Modern game AI concerns itself with AI for video games. The goals are many. Fun and challenging opponents. Autonomous opponents that learn during play and gain new knowledge. Procedural content generation in respect to the player and much more. Not that much has been done in the industry, but in the field there's a lot of focus on machine learning techniques that learn the games themselves based on some criteria set by the creators.

I haven't read anything about AI that attempts to be human-like in the sense they pursued earlier lately. I've read several times however, that the Turing test is faulty and should be ignored; it serves no purpose in the field. The new purpose is to create machines that can do some task, and do it well. If its deemed intelligent by humans is of no consequence. If it does a job better than a human, then it is an advance. That it is worse than optimal is a strength, because as I said, the problems often dealt with are not solvable optimally. (At least not until quantum computing, albeit I know nothing about how that works; it seems to be another new dream, so if its like the dream of AI in the beginning, it will probably not solve all, but just make advances)

It's really good actually. It's part of my collection in "classic" AI.

It doesn't deal with neural networks, evolution or monte carlo sadly. But it does deal greatly with the Intelligent Agent (IA) architecture, which is the foundation of any AI, classic or not. And its chapters on search is superb; and you almost always need search. (Obviously DFS, BFS, Dijkstra, A* etc., are part of normal CS curriculum, but it delves into local search which usually is not part of CS curriculum as it is non-optimal and approximate)

It's also the best book on planning and propositional logics in AI I've read. Haven't had a great need for that myself, but those tools are actually very used in the industry to solve real problems. Some PHD students at my university have made a great local search based container stowage using some iterative local search based inference. It does not always produce an optimal solution, but it does most of the time, and its faster by a large magnitude (solved in a matter of minutes), as the problem is otherwise in NP (unsolvable in polynomial time).

After the book it's easy to jump into the research, because it has introduced you to the terminology. It's introduction chapter is also very nice, as it gives the history of AI research and accomplishments. Gives you an idea of where you are in the field when you read new research.

Oh but a warning; there's no code in the book. There's algorithms written in pseudocode. But it's expected that you implement yourself. If you're a good hacker, that's not that hard, but keep in mind the complexity yourself; the books complexity analysis does not include the data structures etc., so any implementation without the correct tools will be very slow. But AI is really something that should be learned after the CS foundations has been mastered. It does however explain in good detail how the algorithms, and how the theory works. Understand that, and you will have little trouble writing your code, and debugging the system. In my opinion that its much more satisfying, than just to copy a code snippet that you hardly understand. This approach forces you to understand, and therefore master it.

I think they are less durable, but they have potentially higher computing power in their connectivity; within connectivism that is.

Each neuron can independently perform its calculations, even though the transfer of information is quite slow. And we can clearly see it works; animals and humans have very powerful computing capabilities. So I expect organic neural networks to be more powerful, at least short term. In the future we might surpass their capability with computer processors, but it's a different type of processors we have now, and where we are currently going. Basically, it must be a quite slow processor in terms of raw calculations power, but with hundred of thousands of calculation units that can perform independently.

Actually, I think learning a procedural language before an object oriented language is great.

After having done some pretty heavy C programming, and being introduced to C++, it was like a miracle come true. The code stopped being this occluded mess, and things started making sense.

I think Javascript is bad however, because it does not teach about types; learning that everything is just a number of bits, or an array of bits (such as a string), and where they reside in memory is great to know, before going into OO patterns and algorithms in general.