A University of Toronto startup using artificial intelligence to speed drug discovery has raised US$45 million to fund growth – yet another example of how efforts to commercialize the university's AI expertise are making waves in the business world. San Francisco-based Atomwise, which helps screen millions of potential drug candidates in a fraction of the time of traditional methods, said this week its latest funding round was led by Monsanto Growth Ventures, Data Collective (DCVC) and B Capital Group. That brings the total amount of capital Atomwise has raised to more than US$51 million. "With our initial work in 2012, Atomwise became the first startup to commercialize deep neural networks for drug discovery," Abraham Heifets, the company's co-founder and chief executive, said in a statement. "It seemed to many like science fiction then, but now in 2018 Atomwise has the commercial traction with a host of customers to demonstrate our leadership in AI for drug discovery."
Tremendous amounts of data are generated every minute of every day, presenting equal amounts of possibilities and challenges. As we look towards 2017, organisations that understand the growing potential of big data and the need to analyse it efficiently will be the ones that see the greatest success. Here are seven BI strategies that will define the year ahead. It's time we make the most of our BI investments by distributing them to every single person in an organisation. Your sales, marketing and human resources departments can all do their jobs better with the right analytics tools, whether they're sharing competitor information in the cloud, collaborating on sales forecast dashboards to make the most of every potential deal or accessing employment data on their phone in between interviews.
DARPA is soliciting innovative research proposals in the area of semi-automated discovery of machine learning and statistical models and processing pipelines. Proposed research should investigate innovative approaches that enable revolutionary advances in science, devices, or systems. Specifically excluded is research that primarily results in evolutionary improvements to the existing state of practice.
Canon's whitepaper, "Artificial Intelligence Creates E-Discovery Efficiencies, Controls Costs," spotlights how the use of AI lets legal departments do more with less and frees attorneys to spend more time on legal-knowledge-driven work. Many attorneys are familiar with basic eDiscovery tools such as word searches to find relevant documents. AI, however, has moved beyond these basics. One powerful eDiscovery technology is predictive coding, which searches documents for context, concepts and tone. This can significantly increase accuracy and relevance in document review, in some cases enabling tasks to be completed in minutes, not days or months.