Tech Bot Mag

  Recent years have witnessed astonishing progress in generative image modeling, with neural network-based models able to synthesize increasingly realistic and detailed images. This rapid advancement is quantitatively reflected in the steady decrease of Fréchet Inception Distance (FID) scores over time. The FID score measures the similarity between generated and real images based on feature activations extracted from a pretrained image classifier network. Lower FID scores indicate greater similarity to real images and thus higher quality generations from the model. Around 2020, architectural innovations like BigGAN precipitated a substantial leap in generated image fidelity as measured by FID. BigGAN proposed techniques like class-conditional batch normalization and progressive growing of generator and discriminator models to stabilize training and generate higher resolution, more realistic images compared to prior generative adversarial networks (GANs).  The introduction of BigGAN and

  In recent years, brain-machine interface (BMI) technology has captured public imagination, conjuring visions of a cybernetic future where human abilities are enhanced through a direct connection between the brain and computers. At the vanguard of this field is Neuralink, the secretive California-based startup led by billionaire entrepreneur Elon Musk. Neuralink aims to develop ultra-high bandwidth BMIs, which Musk claims could treat neurological conditions, increase human intelligence, and ensure humanity keeps pace with artificial intelligence. After years of animal testing, Neuralink has now reached a historic milestone on the path to this imagined future - their first human trial. Dubbed the Precision Robotic Implantation of Neural Links in Humans (PRIME) study, it recently received U.S. Food and Drug Administration (FDA) approval to begin. This green light sparks a new chapter in BMI research, bringing clinical human testing a step closer. While Neuralink’s progress is undoubtedl

  ChatGPT with computer vision powers? Mind blown! This latest innovation unlocks game-changing skills for AI. We're talking next-gen image search, transforming sketches into apps, bringing fantasies to life - the sky's the limit! But while the hype is real, let's keep things a little closer to Earth: these systems still make rookie mistakes. The emergence of artificial intelligence capabilities that combine computer vision, language processing, and imagination portends a revolutionary inflection point in human progress. ChatGPT’s strengthening aptitude for natural conversation and reasoning now augmented with rudimentary visual analysis skills presages a future where AI assistants help us navigate daily life with human-like versatility. Meanwhile, creative models like DALL-E 3 offer a glimpse into machines that manifest ideas into imagery and narrative with exceptional grace. Together, these innovations represent an exhilarating leap toward artificial general intelligence

Quantum computing has seen remarkable progress in recent years. After the first demonstrations of “quantum supremacy” or “quantum advantage”, companies are now publishing roadmaps projecting commercial quantum computers with millions of qubits within a decade.  Meanwhile, new quantum computing approaches based on photons, atoms and topology are catching up to the early leaders using superconducting circuits and ion traps. The fundamental building blocks of quantum computers are qubits (quantum bits). Unlike regular bits, qubits can exist in a superposition of 0 and 1, allowing massive parallelism. When entangled together, qubits enable certain computations like simulation of quantum systems to be done exponentially faster. The catch is quantum states are fragile and error-prone. The threshold for useful applications is estimated to be between hundreds of thousands to millions of physical qubits. In 2019, Google announced its 53-qubit Sycamore processor achieved quantum supremacy by sam

China's largest semiconductor foundry, Semiconductor Manufacturing International Corporation (SMIC), has recently announced a major breakthrough - mass producing 7nm chips without using the advanced extreme ultraviolet (EUV) lithography machines. SMIC's new 7nm Kirin 9000 mobile processor is designed by Huawei's chip company HiSilicon. It is comparable in performance to Qualcomm's Snapdragon 888 processor built on superior 4nm technology, despite the large process gap.  The Kirin 9000 is used in Huawei's high-end smartphones as an alternative to Qualcomm's market-leading chips. This demonstrates impressive engineering and execution by SMIC to be able to produce advanced 7nm chips using older deep ultraviolet (DUV) lithography tools instead of the latest EUV systems. In reality, the numbers like 7nm, 5nm or 3nm that are used to name process nodes no longer actually refer to any physical transistor dimension on the chips. Below 16nm, these names are

 Artificial intelligence (AI) has made immense strides in recent years, with systems like ChatGPT showcasing just how advanced AI has become. ChatGPT in particular has been upgraded significantly, gaining capabilities that seemed unbelievable just a short time ago. In this extensive article, we'll dive into these new ChatGPT features, including integrated image generation through DALL-E 3, vision capabilities with GPT-4, and an overhauled conversation mode. Beyond ChatGPT, there are many other exciting AI advancements happening. New generative video AI models are producing remarkably smooth and detailed animations. Open source voice cloning now allows near-perfect voice mimicking with just seconds of audio. And video games are being created featuring AI-generated characters that can hold natural conversations. Read on for an in-depth look at these innovations and more. ChatGPT Upgrades: Integration with DALL-E 3 Earlier this year, OpenAI unveiled DALL-E 3, their most advanced image