I explain Artificial Intelligence terms and news to non-experts. Here's BANMo, a NeRF-inspired approach shared at the CVPR 2022 event I attended a few weeks ago. BANMo takes pictures to create deformable 3D models. If you are in VFX, game development, or creating 3D scenes, this new AI model is for you. I wouldn't be surprised to see this model or similar approaches in your creation pipeline very shortly, allowing you to spend much less time, money, and effort on making 3D models.

As Charles Darwin wrote in at the end of his seminal 1859 book On the Origin of the Species, "whilst this planet has gone cycling on according to the fixed law of gravity, from so simple a beginning endless forms most beautiful and most wonderful have been, and are being, evolved." Scientists have since long believed that the diversity and range of forms of life on Earth provide evidence that biological evolution spontaneously innovates in an open-ended way, constantly inventing new things. However, attempts to construct artificial simulations of evolutionary systems tend to run into limits in the complexity and novelty which they can produce. This is sometimes referred to as "the problem of open-endedness." Because of this difficulty, to date, scientists can't easily make artificial systems capable of exhibiting the richness and diversity of biological systems.

In a new study published in the journal Artificial Life, a research team led by Nicholas Guttenberg and Nathaniel Virgo of the Earth-Life Science Institute (ELSI) at Tokyo Institute of Technology, Japan, and Alexandra Penn of The Centre for Evaluation of Complexity Across the Nexus (CECAN), University of Surrey UK (CRESS), examine the connection between biological evolutionary open-endedness and recent studies in machine learning, hoping that by connecting ideas from artificial life and machine learning, it will become possible to combine neural networks with the motivations and ideas of artificial life to create new forms of open-endedness. One source of open-endedness in evolving biological systems is an "arms race" for survival. For example, faster foxes may evolve to catch faster rabbits, which in turn may evolve to become even faster to get away from the faster foxes. This idea is mirrored in recent developments involving placing networks in competition with each other to produce things such as realistic images using generative adversarial networks (GANs), and to discover strategies in games such as Go, which can now easily beat top human players. In evolution, factors such as mutation can limit the extent of an arms race.

Applying filters to images is not a new concept to anyone. We take a picture, make a few changes to it, and now it looks cooler. But where does Artificial Intelligence come in? Let's try out a fun use for Unsupervised Machine Learning with K Means Clustering in Python. I've written before about K Means Clustering, so I will assume you're familiar with the algorithm this time.

Computers have helped us to calculate the vastness of space and the minute details of subatomic particles. When it comes to counting and calculating, or following logical yes/no algorithms – computers outperform humans thanks to the electrons moving through their circuitry at the speed of light. But we generally don't consider them as "intelligent" because, traditionally, computers haven't been able to do anything themselves, without being taught (programmed) by us first. So far, even if a computer had access to all of the information in the world it couldn't do anything "smart" with it. It could find us a picture of a cat – but only because we had told it that certain pictures contain cats.

You hear a lot of different terms bandied about these days when it comes to new data processing techniques. One person says they're using machine learning, while another calls it artificial intelligence. Still others may claim to be doing deep learning, while "cognitive" is the favored phrase for so...

Modern ethos is that all data is valuable, should be stored forever, and that machine learning will one day magically find the value of it. You've probably seen that EMC picture about how there will be 44 zettabytes of data by 2020? Remember how everyone had Fitbits and Jawbone Ups for about a minute? Now Jawbone is out of business. Have you considered this "all data is valuable" fad might be the corporate equivalent?

Remember, just because you get the algorithm to run doesn't mean the result isn't nonsense. If you're new to all of this, then the Machine Learning Foundations course on Coursera is a good place to start -- despite the creepy floating half-professor.

Data scientists are expected to be familiar with the differences between supervised machine learning and unsupervised machine learning -- as well as ensemble modeling, which uses a combination of techniques, and semi-supervised learning, which combines supervised and unsupervised approaches. While it's not necessarily new, deep learning has recently seen a surge in popularity as a way to accelerate the solution of certain types of difficult computer problems, most notably in the computer vision and natural language processing (NLP) fields. By extracting high-level, complex abstractions as data representations through a hierarchical learning process, deep learning models yield results more quickly than standard machine learning approaches. Machine learning, deep learning, and artificial intelligence all have relatively specific meanings, but are often broadly used to refer to any sort of modern, big-data related processing approach.

Computers have helped us to calculate the vastness of space and the minute details of subatomic particles. When it comes to counting and calculating, or following logical yes/no algorithms – computers outperform humans thanks to the electrons moving through their circuitry at the speed of light. But we generally don't consider them as "intelligent" because, traditionally, computers haven't been able to do anything themselves, without being taught (programmed) by us first. So far, even if a computer had access to all of the information in the world it couldn't do anything "smart" with it. It could find us a picture of a cat – but only because we had told it that certain pictures contain cats.