A common strategy for fact-checking long-form content generated by Large Language Models (LLMs) is extracting simple claims that can be verified independently. Since inaccurate or incomplete claims compromise fact-checking results, ensuring claim quality is critical. However, the lack of a standardized evaluation framework impedes assessment and comparison of claim extraction methods. To address this gap, we propose a framework for evaluating claim extraction in the context of fact-checking along with automated, scalable, and replicable methods for applying this framework, including novel approaches for measuring coverage and decontextualization. We also introduce Claimify, an LLM-based claim extraction method, and demonstrate that it outperforms existing methods under our evaluation framework. A key feature of Claimify is its ability to handle ambiguity and extract claims only when there is high confidence in the correct interpretation of the source text.

Mitacs recently asked two leading experts to talk about the intersection of artificial intelligence and clean technology. Ivette Vera-Perez is the Team Lead for Mitacs's Account Management group with expertise in clean technology. Ivette serves on the board of directors of the Ontario Clean Technologies Industry Association (OCTIA).Tibor Turi is the executive director of SOSCIP, a consortium of academic and industry members that supports collaborative research projects through partnership-building services and access to leading-edge advanced computing platforms. Tibor: The current state of AI serving cleantech is both exploratory and operational. It is exploratory because cleantech and AI are both interested in solving critical real-world problems that push researchers and entrepreneurs right to the very edge of innovation.

With AI in a full-fledged mania, 2017 will be the year of reckoning. Pure hype trends will reveal themselves to have no fundamentals behind them. Paradoxically, 2017 will also be the year of breakout successes from a handful of vertically-oriented AI startups solving full-stack industry problems that require subject matter expertise, unique data, and a product that uses AI to deliver its core value proposition. Over the past year a mania has risen up around'bots.' In the technical community, when we talk about bots, we usually mean software agents which tend to be defined by "four key notions that distinguish agents from arbitrary programs; reaction to the environment, autonomy, goal-orientation and persistence." Enterprises have decided to usurp the term'bot' to be mean'any form of business process automation' and create the term'RPA', robotic process automation.

NEXTracker, who makes devices that shift solar panels to soak in as much direct sunlight as possible, acquired a startup called BrightBox Technologies out of Berkeley (shocker) to add some intelligence to its hardware. NEXTracker will use software developed by BrightBox, originally made to monitor and control temperatures in large buildings, to increase energy production of solar farms, thereby enabling faster operations and easier maintenance. Can you say streamlining processes? AI is great for that.

When Facebook, Amazon, IBM, Microsoft and Google team up and form a partnership for the development of a rapidly advancing technology, it's time to start paying attention. You've heard of it, surely. You may know it as Apple's Siri or IBM's Watson. You may know it as Tesla's autopilot. Maybe your mind goes straight to Westworld or Ex Machina.