OpenAI has just announced an enterprise version of its popular generative AI product, ChatGPT. But in this case, OpenAI is a fast follower -- not the first-to-market. Cohere, a Toronto-based company with close ties to Google, is already bringing generative AI to businesses. I spoke with Cohere's President and COO, Martin Kon, about how its machine learning models are being used within enterprise companies. Cohere is only a few years old, but it has an impressive pedigree.

An Oxford University tech spinout has invented a'ground breaking' AI tool that scans users' bodies to provide accurate clothing measurements, with the intention of streamlining the online shopping experience and saving UK retailers billions in returns. Initially founded in 2019 by Duncan McKay, INSEAD MBA and Phil Torr, Professor of Computer Vision and Deep Learning at the University of Oxford, the tech firm went on to be awarded two Innovate UK Grants, and one Future Fashion Factory Grant in partnership with the University of Leeds with funding totalling approximately £1.2 million. McKay said: "I have worked for L'Oreal, Unilever and PepsiCo coming up with new product ideas and consumer solutions – I built an £18m net revenue business in a year whilst at PepsiCo. I got into this because I love innovating – I get a kick out of innovation, building and scaling businesses. I founded Aistetic with Phil Torr as I experienced the problem of poor-fitting clothes personally and we both felt that we could solve this with a technology solution. With the development of our patent-pending solution, we quickly realised that our purpose is bigger than that – we want to make next-gen 3D body modelling available to anyone with a mobile device."

We study how in-context learning (ICL) in language models is affected by semantic priors versus input-label mappings. We investigate two setups-ICL with flipped labels and ICL with semantically-unrelated labels-across various model families (GPT-3, InstructGPT, Codex, PaLM, and Flan-PaLM). First, experiments on ICL with flipped labels show that overriding semantic priors is an emergent ability of model scale. While small language models ignore flipped labels presented in-context and thus rely primarily on semantic priors from pretraining, large models can override semantic priors when presented with in-context exemplars that contradict priors, despite the stronger semantic priors that larger models may hold. We next study semantically-unrelated label ICL (SUL-ICL), in which labels are semantically unrelated to their inputs (e.g., foo/bar instead of negative/positive), thereby forcing language models to learn the input-label mappings shown in in-context exemplars in order to perform the task. The ability to do SUL-ICL also emerges primarily with scale, and large-enough language models can even perform linear classification in a SUL-ICL setting. Finally, we evaluate instruction-tuned models and find that instruction tuning strengthens both the use of semantic priors and the capacity to learn input-label mappings, but more of the former.

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Deploying models at scale can be a cumbersome task for many data scientists and machine learning engineers. However, Amazon SageMaker endpoints provide a simple solution for deploying and scaling your machine learning (ML) model inferences. Our last blog post and GitHub repo on hosting a YOLOv5 TensorFlowModel on Amazon SageMaker Endpoints sparked a lot of interest from our readers. Many readers were also interested in learning how to host the YOLOv5 model using PyTorch. To address this issue and with the recent release of the YOLOv8 model from Ultralytics, we present this post on how to host a YOLOv8 PyTorchModel on SageMaker endpoints.

This post presents and compares options and recommended practices on how to manage Python packages and virtual environments in Amazon SageMaker Studio notebooks. A public GitHub repo provides hands-on examples for each of the presented approaches. Amazon SageMaker Studio is a web-based, integrated development environment (IDE) for machine learning (ML) that lets you build, train, debug, deploy, and monitor your ML models. Studio provides all the tools you need to take your models from data preparation to experimentation to production while boosting your productivity. Studio notebooks are collaborative Jupyter notebooks that you can launch quickly because you don't need to set up compute instances and file storage beforehand.

Companies from Meta to Home Depot are flooding earnings calls with commentary about their artificial intelligence efforts. Ever since OpenAI's ChatGPT lit up the internet in November, companies can't stop talking about artificial intelligence. Take this earnings season so far: References to AI and related terms during calls with investors are already up 77% from a year earlier. AI-hungry investors have propelled Nvidia Corp., which makes the chips needed for complex AI computing tasks, into the best-performing stock among mega-caps this year. Relatively obscure firms with AI in their names have also skyrocketed.

Chatbots are disrupting industries in unexpected ways -- including retail. The latest evidence: A leaked memo from Walmart Global Tech warning against sharing confidential corporate or customer information with bots like ChatGPT. Business Insider reports that in a memo issued Tuesday, the big box store's tech arm said ChatGPT had been blocked following "activity that presented risk to our company." Insider indicated it viewed the memo and stated that Global Tech had evaluated and developed "usage guidelines around generative AI tools and are now opening ChatGPT for usage within the Walmart network." A Walmart spokesperson Insider contacted for comment didn't address blocking AI-powered bots but issued a statement that said in part that "new technologies present new benefits as well as new risks."

There's this odd feeling that starts at the back of the neck. It feels like the hairs are raising up slightly. The first time I felt it was in the mid-70s. I was in high school. I was sitting in front of an ASR-33 teletype machine and I hit something, probably the RE-TURN key. That's how it was spelled.

A well-designed recommender system can accurately capture the attributes of users and items, reflecting the unique preferences of individuals. Traditional recommendation techniques usually focus on modeling the singular type of behaviors between users and items. However, in many practical recommendation scenarios (e.g., social media, e-commerce), there exist multi-typed interactive behaviors in user-item relationships, such as click, tag-as-favorite, and purchase in online shopping platforms. Thus, how to make full use of multi-behavior information for recommendation is of great importance to the existing system, which presents challenges in two aspects that need to be explored: (1) Utilizing users' personalized preferences to capture multi-behavioral dependencies; (2) Dealing with the insufficient recommendation caused by sparse supervision signal for target behavior. In this work, we propose a Knowledge Enhancement Multi-Behavior Contrastive Learning Recommendation (KMCLR) framework, including two Contrastive Learning tasks and three functional modules to tackle the above challenges, respectively. In particular, we design the multi-behavior learning module to extract users' personalized behavior information for user-embedding enhancement, and utilize knowledge graph in the knowledge enhancement module to derive more robust knowledge-aware representations for items. In addition, in the optimization stage, we model the coarse-grained commonalities and the fine-grained differences between multi-behavior of users to further improve the recommendation effect. Extensive experiments and ablation tests on the three real-world datasets indicate our KMCLR outperforms various state-of-the-art recommendation methods and verify the effectiveness of our method.