GSLM: First Audio High-Performance NLP Model Accuracy of Different Models on ImageNet Database ( Source )īenefit: It is one step closer to real AI applications. Moreover, Perceiver’s one part deals with actual data while the other looks at the summary and reduces training time exponentially. It converts all inputs of different formats, including audio, image, or sensor data, into bytes, making them instantly available to work with any data. Perceiver has a cross-attention layer instead of self-attention to overcome time complexity. However, its disadvantage includes overfitting (cannot be ignored in similar big models), but scientists tried to overcome it greatly. It achieved almost twice the accuracy of the ResNet-50model while sharing image classification results. Unlike existing models, it deals with multiple types of data. It follows the human brain principle in receiving, analyzing, and processing data simultaneously in several formats. Perceiver: Compatible with Multimodal DataĭeepMind developed this transformer-based model that can deal with multimodal data like humans. One of its exciting features is its computational cost-effectiveness. TimeSformer is based on a self-attention mechanism allowing the model to understand space-time dependencies during the whole video. TimeSformer in Working ( Source )īenefit: The developed architecture is measurable and helpful in creating more accurate models. Currently, available models are suitable for seconds-long videos. It is a big step towards processing comparatively large videos. It splits the video into small non-overlapping patches and avoids exhaustive comparisons among patches via self-attention. It shows comparatively higher speeds being the very first architecture based on transformers. Time-Space Transformer developed by Facebook AI is an exclusively new technique for video understanding. TimeSformer: New Video Architecture Approach Plus, it draws the internal structure of a specific entity which requires perfect knowledge and is not possible without proper training. Image Generated by DALL-E ( Source )īenefit: This neural network controls the number of times an attribute appears on the image and visualizes each aspect in 3D models. Its training is divided into two broad categories: in the first step, the image is compressed (transformer size is reduced without compromising quality), while the second step involves chain series (256-byte pair encoding of text tokens with image tokens autoregressive transformer training). A sequence of tokens (discrete vocabulary symbols) is used as input, and the model is trained to multiply the chances of sequential token generation. It took autoregressive transformer parameters of about 12 billion from GPT-3. It is trained over 259 million text pairs and images collected from the internet. It produces anthropomorphized versions of various objects, including animals. It works unlike the previous advancement in the same field as having great knowledge of unseen text content. Interestingly, it is not based on GANs commonly used to train neural networks for image generation, making it an incredibly new approach. It comes with zero-shot performance for generating images of absurd and unclear objects with extraordinary quality. DALL-EĭALL-E neural network is a breakthrough in computer vision developed by OpenAI in 2021 that involves creating images from text content. Let's have a look at top most amazing achievements of Artificial Intelligence in year 2021. Recent studies show that companies supporting the economy in China, North Africa, and the Middle East installed artificial intelligence solutions. 56% of all business holders (the number was 50% in 2020) reported adopting artificial intelligence in one of their functions. Facial detection, text generation, speech recognition, drug discovery, and automated translation feature are some of the AI-worthy achievements in 2021.Īccording to a 2021 survey, McKinsey reported that AI adoption is rising continually.
Tech giants have already adopted AI solutions in their systems to improve user experience, engagement, services, and product delivery. Its market size is forecasted to reach $266.92 billion with a CAGR (Compound Annual Growth Rate) of 33.2% by 2027. Artificial intelligence technology has exceptional benefits and potential for future automation and prosperity.
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