February 2023 Summaries

In February 2023, large language models (LLMs) experienced significant advancements with Google's announcement of Bard and Microsoft's integration of a customized GPT model into its Bing search engine and Edge browser. These LLMs are connected to constantly-updating search indices, allowing them to function as conversational search assistants. However, they have also been known to relay false information. Meta introduced LLaMA on February 24th, which is smaller and more efficient than other large language models. While these LLMs may not be great at telling the truth, they are good for creative tasks such as writing and coding, and offer a unique form of entertainment through their conversational capabilities.

Researchers are using deep learning and bioacoustics to study orca whales, as they spend only about 5% of their time near the surface where scientists can observe them. With advancements in technology, hydrophones have become cheaper, smaller, and more durable, allowing researchers to collect massive amounts of ocean recordings. Deep Neural Networks (DNNs) similar to those used in automated speech-to-text models are being employed to parse out orca calls from the reels of hydrophone recordings. ORCA-SPOT is a convolutional neural network model that can automatically find orca vocalizations, boasting a 93.2% accuracy rate. AI models like ORCA-SPOT help researchers better understand orcas' communication and behavior, prevent ship-orca collisions, and enlist citizen scientists in labeling ocean noise data for further research.

The intermediate AI dictionary explores advanced AI models, algorithms, and techniques, building on foundational concepts like neural networks and vectorization. It covers prominent AI models such as ChatGPT, Bard, and Bing Chat, which are conversational agents, and Dall-E and Stable Diffusion, which focus on text-to-image transformations. The guide also delves into AI frameworks like PyTorch and TensorFlow, used for designing and deploying AI models, and explains different AI algorithms, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), which are essential for tasks like image recognition and sentence processing. Various learning types, including supervised, unsupervised, semi-supervised, and zero-shot learning, are discussed, highlighting their applications in animal species identification. The text concludes with examples of tasks AI can perform, such as sentiment analysis and classification, emphasizing the complexity and beauty derived from relatively simple mathematical principles and data structures.

The article discusses the potential demise of third-party cookies, a tool used for tracking consumer behavior online without their consent or knowledge. These cookies are being phased out by companies like Apple and Mozilla due to increasing privacy concerns from consumers. This shift could lead to an increased reliance on artificial intelligence as a means of analytics in place of cookies. Advertisers will be most affected by this change, as they will need to find new ways to access similar data sets.

This tutorial demonstrates how to create a basic Next.js and Deepgram web app that transcribes audio files into text using the Deepgram API. The project utilizes Repl.it, an instant IDE running in the browser, and requires understanding of JavaScript, React, HTML, CSS, and familiarity with hooks. The final product allows users to input a link to an audio file, which is then transcribed and displayed on the web page. The transcription includes speaker diarization for podcasts featuring multiple speakers.

The article discusses the repeated boom-and-bust cycles of Artificial Intelligence (AI) since its inception and how these cycles can be avoided. It highlights that AI research has often underestimated the complexity of human intelligence, leading to overconfidence and unrealistic promises. Furthermore, dominant schools of thought have sometimes overshadowed exploration beyond what was in fashion, causing idea entrenchment. The article also discusses the role of government funding in these cycles and suggests that AI students, researchers, and business leaders can help avoid future winters by focusing on testing, communication, interdisciplinary knowledge, and remaining open to novel paradigm shifts.

Google is set to release its own AI language model called Bard, which has been compared to OpenAI's ChatGPT. Both models are based on transformers and have unique training styles. While ChatGPT was initially trained on a large dataset of written texts before being fine-tuned for conversation, Bard was designed from the start to read dialogue. In terms of size, both models seem to be in the same weight class, with GPT-3.5 having around 175 billion parameters and LaMDA containing up to 137 billion. However, Google has made efforts to improve factual correctness in Bard's responses by incorporating information retrieval systems. The upcoming battle between these two AI models is expected to be interesting, with both having their unique strengths and potential areas of improvement.

The Developer's Guide to Speech Recognition in Python discusses the importance of speech recognition and its applications in modern technology. It provides an overview of various Python libraries such as SpeechRecognition, PyAudio, Librosa, Deepgram, Pysptk, Parselmouth, Audacity, Speechbrain, and Torchaudio that can be used for speech recognition, audio processing, and music analysis. The article also highlights the features required from a good STT solution and provides examples of common applications of these APIs.

This article analyzes the talk time of late-night TV hosts such as Stephen Colbert, Conan O'Brien, Jimmy Fallon, Jimmy Kimmel, and Seth Meyers. Using deep learning and data science techniques, it is found that on average, talk show hosts speak more than their guests 40% of the time. The hosts' share of talking in their interviews varies from Stephen Colbert at 54.4% to Seth Meyers at 38.7%. The analysis also reveals interesting facts about specific guests and their interactions with the hosts.

Large Language Models (LLMs) are becoming increasingly integrated into companies' product offerings due to their superior capabilities compared to previous neural network-based models. They can perform reasonably well with only a few labeled samples of data and scale adequately with an increase in parameters and training data. Core application areas for LLMs include summarization, translation, conversational AI, and text-to-code generation. As these models grow larger and more sophisticated, their capacity to serve as human-computer interfaces for various tasks becomes a potential reality.