To understand AI Bias, we need to understand Dataset Bias. Collecting, labelling, and organizing data is a time consuming and expensive effort. Many popular datasets in the artificial intelligence community can take years to produce and publish. This effort requires a large amount of resources, and does not make dataset creation a small or efficient task. Since it’s impractical to create a dataset with all possible permutations and domains, all datasets have some form of bias in them. This limitation in data causes lower performance and decreased generalization across unrepresented domains.
The simple answer is to create more data, but…
Recent research trends in Artificial Intelligence, Machine Learning, and Computer Vision have led to a growing research space called Embodied AI. Facebook AI Research (FAIR) and Intel Labs has been spearheading new projects in the space of Embodied AI. “Embodied” is defined as “giving a tangible or visible form to an idea.” Simply put, “Embodied AI” means “AI for virtual robots.” …
Just imagine two enemy opponents fighting each other in a game. If they are having a boxing match, then they might be punching each other’s head. The initial punch will knock the face in the direction of the punch’s force.
You might have heard of Google DeepMind beating several Atari games, beating a professional Go player, or even teaching a humanoid simulation to walk. This has been achieved with the help of Reinforcement Learning.
Reinforcement Learning is a subcategory of Machine Learning where an agent learns to behave in an environment. Other popular subcategories of Machine Learning are Supervised Learning and Unsupervised Learning. Reinforcement Learning is different than supervised learning and unsupervised learning, in that the sequence of actions you take to achieve your goal is important to the problem at hand.
Supervised learning is done using ground truth. Thus…
Are you looking for Inverse Dynamics in Unity or Unity’s Physics engine (PhysX)? It doesn’t exist as far as I can tell, but it does exist for other physics engines, such as Bullet and possibly ODE (Open Dynamics Engine). Luckily, Inverse Dynamics is not too hard to implement in Unity.
Unity does have an IK (Inverse Kinematics) feature, but that only helps you with kinematic motion and not with physically-based motion. You don’t want your humanoid character to just move, you also want him to move realistically. That’s where Inverse Dynamics comes in. …
Are you interested in the physics behind a humanoid character or perhaps some other animal? Then you might want to know the forces and torques required to move an arm or leg from one position to another position. Inverse Dynamics computes the forces and torques of an articulated body. This is traditionally very useful in robotics, since you need to know the joint torques required to rotate a link from one point in space to another. In animation, this helps to create a physically realistic response from the character.
If you’ve heard about Artificial Intelligence, Machine Learning, or Deep Learning recently, then you might have heard of a Neural Network.
Neural Networks are a key piece of some of the most successful machine learning algorithms. The development of neural networks have been key to teaching computers to think and understand the world in the way that humans do. Essentially, a neural network emulates the human brain. Brains cells, or neurons, are connected via synapses. This is abstracted as a graph of nodes (neurons) connected by weighted edges (synapses).
So let’s dive in. What is a neural network? The human…
If you’re in tech, you’ve been hearing a lot of buzz around Artificial Intelligence, Machine Learning, and even Deep Learning. What’s the right word to be using and when? Do they all mean the same thing? I mean, people are sure using it interchangeably all the time.
Artificial Intelligence, Machine Learning, and Deep Learning are each a subset of the previous field. Artificial Intelligence is the overarching category for Machine Learning. And Machine Learning is the overarching category for Deep Learning.
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