Self-supervised learning is a form of unsupervised learning in which the supervised learning task is constructed from raw, unlabeled data. Supervised learning is effective but usually requires a large amount of labeled data. Getting high-quality labeled data is time-consuming and resource-intensive, especially for sophisticated tasks like object detection and instance segmentation, where more in-depth annotations are sought.

Self-supervised learning aims to first learn usable representations of the data from an unlabeled pool of data by self-supervision and then to refine these representations with few labels for the supervised downstream tasks such as image classification, semantic segmentation, etc.

Self-supervised learning is at the heart of many recent advances in artificial intelligence. However, existing algorithms focus on a particular modality (such as images or text) and a high computer resource requirement. Humans, on the other hand, appear to learn significantly more efficiently than existing AI and to learn from diverse types of information consistently rather than requiring distinct learning systems for text, speech, and other modalities.

DALL-E 2 transformed the world of art in 2022.

DALL-E is a system that has been around for years, but its successor, DALL-E 2, was launched this year.

Ibrahim Can/Interesting Engineering.

DALL-E and DALL-E 2 are machine-learning models created by OpenAI to produce images from language descriptions. These text-to-image descriptions are known as prompts. The system could generate realistic images just from a description of the scene. DALL-E is a neural network algorithm that creates accurate pictures from short phrases provided by the user. It comprehends language through textual descriptions and from “learning” information provided in its datasets by users and developers.

Turing Award winner and deep learning pioneer Geoffrey Hinton, one of the original proponents of backpropagation, has argued in recent years that backpropagation does not explain how the brain works. In his NeurIPS 2022 keynote speech, Hinton proposes a new approach to neural network learning: the Forward-Forward algorithm.

After six decades we have finally reached controlled fusion “ignition.” Here is how it works and what it means (and doesn’t mean):

At the Lawrence Livermore National Lab (LLNL) the National Ignition Facility (NIF) starts with the Injection Laser System (ILS), a ytterbium-doped optical fiber laser (Master Oscillator) that produces a single very lower power, 1,053 nanometer (Infrared Light) beam. This single beam is split into 48 Pre-Amplifiers Modules (PAMs) that create four beams each (192 total). Each PAM conducts a two-stage amplification process via xenon flash lamps.

Self-coding and self-updating AI algorithms appear to be on the horizon. There are talks about Pitchfork AI, a top-secret Google Labs project that can independently code, refactor, and use both its own and other people’s code.

This type of AI has actually been discussed for a long time, and DeepMind mentioned it at the beginning of the year along with the AlphaCode AI, which, according to them, “code programs in competitive level” as a middle developer. However, since February, there hasn’t been any more interesting news.

Search Engine Optimization (SEO) is the process of optimizing on-page and off-page factors that impact how high a web page ranks for a specific search term. This is a multi-faceted process that includes optimizing page loading speed, generating a link building strategy, as well as learning how to reverse engineer Google’s AI by using computational thinking.

Computational thinking is an advanced type of analysis and problem-solving technique that computer programmers use when writing code and algorithms. Computational thinkers will seek the ground truth by breaking down a problem and analyzing it using first principles thinking.

Since Google does not release their secret sauce to anyone, we will rely on computational thinking. We will walk through some pivotal moments in Google’s history that shaped the algorithms that are used, and we will learn why this matters.