The Chicago Urban League is partnering with ILLInet RECOVER in an effort to learn more about how COVID 19 is affecting our community.

Physicists prove that classical machine learning models can improve predictions about quantum materials.

Electronic Health Records, EHR, Artificial Intelligence, AI, Machine Learning, Internet of. One major recurring challenge faced by organizations …

Machine Learning as a Service (MLaaS) Market Innovative Strategy by 2030 | Amazon, Oracle, IBM, Microsoftn, Google. Photo of Mark MarkOctober 4, 2022.

AssemblyAI is a remote-first AI company building powerful deep learning models for developers, startups, and enterprises to transcribe and understand their audio data. Our ASR models already outperform companies like Google, AWS, and Microsoft – which is why hundreds of companies and thousands of developers are using our APIs to transcribe and understand millions of videos, podcasts, phone calls, and zoom meetings every day. Our APIs power innovative products like conversational intelligence platforms, zoom meeting summarizers, content moderation, and automatic closed captioning. AssemblyAI's Speech-to-Text APIs are already trusted by Fortune 500s, startups, and thousands of developers around the world, with well-known customers including Spotify, Algolia, Dow Jones, Happy Scribe, BBC, The Wall Street Journal, and NBCUniversal. As part of a huge and emerging market, AssemblyAI is well on its way to becoming the leader in speech recognition and NLP.

Speech is the fundamental mode of human communication, and its synthesis has long been a core priority in human-computer interaction research. In recent years, machines have managed to master the art of generating speech that is understandable by humans. But the linguistic content of an utterance encompasses only a part of its meaning. Affect, or expressivity, has the capacity to turn speech into a medium capable of conveying intimate thoughts, feelings, and emotions -- aspects that are essential for engaging and naturalistic interpersonal communication. While the goal of imparting expressivity to synthesised utterances has so far remained elusive, following recent advances in text-to-speech synthesis, a paradigm shift is well under way in the fields of affective speech synthesis and conversion as well. Deep learning, as the technology which underlies most of the recent advances in artificial intelligence, is spearheading these efforts. In the present overview, we outline ongoing trends and summarise state-of-the-art approaches in an attempt to provide a comprehensive overview of this exciting field.

Considering composers usually write musical pieces in In recent years, machine learning techniques have been a hierarchical manner [25], we note that prompt-based conditioning widely applied to symbolic music generation. A large approaches have a strong limitation: they generate number of models attain sequential generation by accounting results with only consideration of local smoothness for only the past context, i.e., the generated music depends among the past context, future context, and result, without on only the preceding musical content [1-14]. While taking care of the overall musical form or structure of the sequential generation can find useful use cases, it does not music. For instance, a composer may like to write a song align with typical human compositional practices which in a musical form of ABA'B'. If we consider the concatenation can be non-sequential in nature. Musicians often write motifs of the segments corresponding to A and B (i.e., AB) or small pieces to get inspiration first, before working as the past context, and the segment corresponding to B' on the middle parts to connect them.

Sets have been used for modeling various types of objects (e.g., a document as the set of keywords in it and a customer as the set of the items that she has purchased). Measuring similarity (e.g., Jaccard Index) between sets has been a key building block of a wide range of applications, including, plagiarism detection, recommendation, and graph compression. However, as sets have grown in numbers and sizes, the computational cost and storage required for set similarity computation have become substantial, and this has led to the development of hashing and sketching based solutions. In this work, we propose Set2Box, a learning-based approach for compressed representations of sets from which various similarity measures can be estimated accurately in constant time. The key idea is to represent sets as boxes to precisely capture overlaps of sets. Additionally, based on the proposed box quantization scheme, we design Set2Box+, which yields more concise but more accurate box representations of sets. Through extensive experiments on 8 real-world datasets, we show that, compared to baseline approaches, Set2Box+ is (a) Accurate: achieving up to 40.8X smaller estimation error while requiring 60% fewer bits to encode sets, (b) Concise: yielding up to 96.8X more concise representations with similar estimation error, and (c) Versatile: enabling the estimation of four set-similarity measures from a single representation of each set.

We explore the idea of compressing the prompts used to condition language models, and show that compressed prompts can retain a substantive amount of information about the original prompt. For severely compressed prompts, while fine-grained information is lost, abstract information and general sentiments can be retained with surprisingly few parameters, which can be useful in the context of decode-time algorithms for controllability and toxicity reduction. We explore contrastive conditioning to steer language model generation towards desirable text and away from undesirable text, and find that some complex prompts can be effectively compressed into a single token to guide generation. We also show that compressed prompts are largely compositional, and can be constructed such that they can be used to control independent aspects of generated text.

Depth estimation is critical for any robotic system. In the past years estimation of depth from monocular images have shown great improvement, however, in the underwater environment results are still lagging behind due to appearance changes caused by the medium. So far little effort has been invested on overcoming this. Moreover, underwater, there are more limitations for using high resolution depth sensors, this makes generating ground truth for learning methods another enormous obstacle. So far unsupervised methods that tried to solve this have achieved very limited success as they relied on domain transfer from dataset in air. We suggest training using subsequent frames self-supervised by a reprojection loss, as was demonstrated successfully above water. We suggest several additions to the self-supervised framework to cope with the underwater environment and achieve state-of-the-art results on a challenging forward-looking underwater dataset.