1-4. Intro to Artificial Intelligence

To help us understand this phenomenon we’ll take the help of an old friend, probability theory. It gives us a formal mechanism to capture uncertainty in the real world. And let’s us make sensible decisions, even when we are not fully sure about something. >> Since the assignment of cars and goats is random, it … Read more

It turns out that if you consistently stay with the original door, you only win about one-third or about 33 percent of the time. Whereas if you always switch, you win about two-thirds or about 66 percent of the time. So switch is indeed a better overall strategy for this game.

This conversation reminds me of a game show that has a very interesting and non intuitive problem associated with it. >> Are you thinking the Monty Hall problem? >> Yes, I am. You’re shown three doors, behind one of them is a shiny new car, while the other two have a goat. These are randomly … Read more

What’s really interesting in this representation is, that the edges embody the rules of the game. For instance, you can only go to a finite number of next states from each current state. >> Hm, you’re right. It limits the number of moves we might need to consider, especially later in the game, when the … Read more

The first option of denoting each cell as a node is fairly intuitive, assuming that we interpret the node as representing the current position of the computer player. But, does that capture enough information for you to decide what to do next? Not really. So, I guess this is not a good representation of this … Read more

Our goal is to use a search strategy similar to the route-finding problem, but to design an AI that can play Tic Tac Toe. What, in your opinion, should be the nodes and edges in the graph that we search? Option A, should each cell be a separate node, with an edge connecting two of … Read more

Finding the shortest path on a map is a very useful ability and that heuristic is a nice trick. But it seems more like an optimization problem to me. I’m not sure if I’d consider that a sign of intelligence. >> Mm, okay, how would you characterize an intelligence system or agent? >> Well, unlike … Read more

Navigation is an excellent example of a computationally hard problem that AI can help solve more efficiently. >> What do you mean by computationally hard, problems that have a high time or space complexity? >> Yeah, precisely. Let me illustrate. I’m trying to plan a road trip from Manchester to Sheffield in the UK and … Read more

If all this sounds exciting to you, you’re in the right place. This nano degree program is organized into two parts, starting with this term. >> Here in term one, we’ll cover the fundamental principles of AI, search optimization, logic, planning, probability, and so on. No matter where you go in AI, these form your … Read more

Okay, let’s get back to our question of what is intelligence? Given these building blocks, agent, environment, perception, action, and cognition, is there a more practical definition that we can come up with? >> Well here’s one formulation that’s actually quite widely accepted. An intelligent agent is one that takes actions to maximize its expected … Read more

So if we’re playing poker, that would be partially observable, I think, because you can see your own hand, but you can’t see your opponents’ hands. >> That’s true. >> It would be stochastic, because you don’t know what cards are going to be dealt and when. It would be discrete, because there are only … Read more

One way to classify AI problems is using properties of the environment and components of state that need to be captured. >> An environment can be fully observable like Tic-Tac-Toe where you can see the entire board. Or could be partially observable like in Battleship where you can’t see your opponents ship positions. >> An … Read more

An agent interacts with the environment by sensing its properties. This is known as perception and by producing useful output or actions that typically change the state of the environment. The process by which an agent decides what action to take based on its perceived inputs is called cognition. Much of our discussion of AI … Read more

Understanding the task or problem domain is key to designing intelligent systems. We call it the environment, and we refer to the intelligence system or software itself as the agent. >> For instance, consider the Roomba. An automated vacuum cleaner that moves around the house in a consistent pattern sucking up dirt. Its environment consists … Read more

What if I was not human and still behaved the way I did? Would you still consider me intelligent? >> You know actually you’re right, I wouldn’t be able to tell the difference. >> Then perhaps there are certain qualities of systems, both artificial and biological that we ascribe the notion of intelligence to >> … Read more

Here’s what I think. A rock, no way. They don’t sense anything. They can’t think. They can’t act. Now, a plant is alive, but it’s not really intelligent. Well, plants can sense and grow towards the light source to maximize food production and their chances of survival. Okay. If you put it that way, but … Read more

Perhaps this is the true goal of intelligence. To be able to produce reasonable behavior while dealing with different sources of complexity. >> I like that. But what exactly is intelligence then? >> Boy, okay, let’s look at some examples and you tell me whether you consider them as intelligent or not. How about a … Read more

Hi, and welcome to Artificial Intelligence. I’m David Joyner. >> And I’m Arpan. We’re here to get you started on this exciting subject. AI has been a cornerstone in my research, from cognitive modelling to robotics challenges. In my PhD, I focused on computer vision, and how it is used by intelligent agents to accomplish … Read more