Graph theory is an integral component of algorithm design that underlies sparse matrices, relational databases, and networks. Improving the performance of graph algorithms has direct implications to applications ranging from operations research to computational biology. As a result, the design of faster graph algorithms has received extensive attention, leading to tools with wide reaching implications. One of the most well studied problems in graph algorithms is the shortest path problem. Given weights on edges, compute the shortest path with minimum total weight from vertex s to vertex t.

Context-aware Machine Translation aims to improve translations of sentences by incorporating surrounding sentences as context. Towards this task, two main architectures have been applied, namely single-encoder (based on concatenation) and multi-encoder models. In this study, we show that a special case of multi-encoder architecture, where the latent representation of the source sentence is cached and reused as the context in the next step, achieves higher accuracy on the contrastive datasets (where the models have to rank the correct translation among the provided sentences) and comparable BLEU and COMET scores as the single- and multi-encoder approaches. Furthermore, we investigate the application of Sequence Shortening to the cached representations. We test three pooling-based shortening techniques and introduce two novel methods - Latent Grouping and Latent Selecting, where the network learns to group tokens or selects the tokens to be cached as context. Our experiments show that the two methods achieve competitive BLEU and COMET scores and accuracies on the contrastive datasets to the other tested methods while potentially allowing for higher interpretability and reducing the growth of memory requirements with increased context size.

Everything these days is done at a faster pace / except, it seems, the sales cycle. With all the data it generates, sales is a shoo-in for AI assistance. For many organizations, though, adopting an AI-enhanced sales approach seems more sci-fi than savvy solution. But other early adopters have found that implementing AI in three areas (process automation, sales guidance, conversation guidance) has a remarkable effect on their sales cycle. The trick is to automate time-wasting tasks, freeing up more time for sales reps and teams to strategize and sell.

As we said before, some organizations are hanging back, unsure if AI is worth the hype and the expense. But others are adopting it, with great results. This is the time to explore what AI can do for your sales team!

Researchers say that they are on the verge of cracking the aging code. They also say that death caused by aging may become obsolete by 2033 if artificial intelligence gets involved. Scientists from the Maximum Life Foundation believe that the combination of two fields of science will cure aging. They also believe that this combination will keep us healthy and young. These fields are artificial intelligence and gene therapy. Researchers at Scicog Systems are creating an'Artificial Scientist'.