I am not a professional but I have been thinking a lot about AI and neural nets, so I thought I might add my 2 ct.
"Acting intelligently" or "deriving goal directed action from sensory input" is actually a lot more complex than what computer processors have been doing so far. I think we are on a good path right now, but it will still be quite a while before we can have AI act in the real world.
First of all, it does not help your agent much if it has all the five senses (or more), because it will just be drowning in information. Real brains are ignoring the majority of data that they receive. Right now, you are not thinking about your breathing, heart beat, toes, nostrils, elbows, the noise outside or your clothes. An artificial intelligence needs a sense for what is worth paying attention to, otherwise there is just too much to compute.
But the next problem is that it is really hard to find out what is irrelevant and what is important. Deep learning teaches neural networks what inputs are relevant or irrelevant by training them to reach defined goals. The network basically tries randomly changing its computational layout and memorizes what gets it closer to the goal and what doesn't. In the end the neural net can react appropriately to a set of known inputs, which have a defined desired output. Although this is a good start, because it models the evolutionary process, it is still very basic, because the expected outputs are still pre-defined. As soon as something happens that it wasn't trained to do, the neural network doesn't know how to react. You would have to train it with every possible scenario in the real world, which is virtually impossible.
So the agent does not only have to figure out what it should ignore at a given point in time, it also has to figure out why it should ignore or why it should act by itself.
If you think about it, what we are trying to do is to artificially replicate human intellect, or at least primitive intelligence. So we need to model the evolution of an animal brain in the real world. Just like animals, each AI agent, an offspring of a specific (genetic) layout, has to be thrown out into a field of laws in which it can act. By selecting layouts based on the predefined laws, mixing them and mutating them randomly, agents which better fulfill the selection criteria become more common.
An approach like this would be necessary to replicate the entire complexity of a brain. You can't design the brain yourself, bottom up, because there is just too many possible scenarios to cover. You have to model the entire environment you want your AI to act intelligently in and leave it to evolve and design itself, which is virtually impossible if the environment is the physical world.
An outstanding source about the big picture of psychology, neuroscience, cybernetics and artificial intelligence are Jordan Petersons university lectures. His knowledge helped me a lot with trying to understand the brain.