This paper presents the Ensemble Nucleotide Byte-level Encoder-Decoder (ENBED) foundation model, analyzing DNA sequences at byte-level precision with an encoder-decoder Transformer architecture. ENBED uses a sub-quadratic implementation of attention to develop an efficient model capable of sequence-to-sequence transformations, generalizing previous genomic models with encoder-only or decoder-only architectures. We use Masked Language Modeling to pre-train the foundation model using reference genome sequences and apply it in the following downstream tasks: (1) identification of enhancers, promotors and splice sites, (2) identification of biological function annotations of genomic sequences, (3) recognition of sequences containing base call mismatches and insertion/deletion errors, an advantage over tokenization schemes involving multiple base pairs, which lose the ability to analyze with byte-level precision, and (4) generating mutations of the Influenza virus using the encoder-decoder architecture and validating them against real-world observations. In each of these tasks, we demonstrate significant improvement as compared to the existing state-of-the-art results.

Did you miss a session at the Data Summit? AIops -- the practice of applying AI to automate and improve IT operations -- has gained currency during the pandemic. As businesses embrace digital transformation strategies involving "multicloud," or the use of services from more than one cloud vendor, there's an increasing need to improve the observability and analytics around networking infrastructure and performance. In a 2022 Nutanix survey, organizations cited interoperability, security and data integration as the top challenges in managing mutlicloud setups. Spurred by the challenge, Kannan Kothandaraman and Nitin Kumar -- both networking industry veterans -- in 2019 launched Selector, an AIops platform for network, cloud and app delivery workflows.

A study of 100 people from a variety of ages and technical aptitudes conducted at business school Bentley University in Massachusetts found that just as with other humans, people form first impressions of chatbots that stick. The study was conducted as part of the school's Human Factors and Information Design program, by students working under Bentley adjunct lecturer Meena Kothandaraman, founder of the twig fish research practice, and in partnership with NeuraFlash, a Boston-based firm that uses AI for Salesforce consulting. The takeaway for botbuilders is obvious: However sophisticated your bot, or whether it hangs out with Maroon 5, its first greeting and responses to new contacts will determine whether they find it an automated annoyance or a new best friend.