We present a system that utilizes machine learning for tactic proof search in the Coq Proof Assistant. In a similar vein as the TacticToe project for HOL4, our system predicts appropriate tactics and finds proofs in the form of tactic scripts. To do this, it learns from previous tactic scripts and how they are applied to proof states. The performance of the system is evaluated on the Coq Standard Library. Currently, our predictor can identify the correct tactic to be applied to a proof state 23.4% of the time. Our proof searcher can fully automatically prove 39.3% of the lemmas. When combined with the CoqHammer system, the two systems together prove 56.7% of the library's lemmas.

Imagine a typical Tuesday morning. The lot is full, with vehicles parked nose to nose. You wait in a long line, similar to what you would see at Disney on a typical weekend. When you finally get inside, you see rows upon rows of empty shelves. You maneuver your shopping cart around frenzied shoppers, only to find that Costco is out of face masks, nonperishable items, medications, hand sanitizers, and hand soaps. The fear and panic as the coronavirus (known as '2019-nCov' or'Covid-19) spreads globally.

When chief information officers think about their organizations and where machine learning might be deployed, the process often begins with an inventory of tasks. The CIOs and department leaders identify routine, repeatable processes that humans can pass off to computers. Then the operations and IT teams set up targeted programs to make those tasks more efficient. As legendary CIO Paul Strassmann has pointed out -- not without controversy -- it's a piecemeal approach that has become standard practice in most businesses. Strassmann's career includes serving as NASA's CIO from 2001 to 2003 and serving in an equivalent role in the Pentagon before that.

With more than 2000 spoken languages, Africa's linguistic diversity is second only to Asia. A third of the world's languages is spoken by the 1.2 billion people living within her 54 countries. But the language of artificial intelligence is yet to gain fluency. It has become hackneyed to weave AI into every conversation about technology and society. AI will take away jobs.

If recent television shows are anything to go by, we're a little concerned about the consequences of technological development. Black Mirror projects the negative consequences of social media, while artificial intelligence turns rogue in The 100 and Better Than Us. The potential extinction of the human race is up for grabs in Travellers, and Altered Carbon frets over the separation of human consciousness from the body. And Humans and Westworld see trouble ahead for human-android relations. Narratives like these have a long lineage.

Recurrent neural networks are extremely appealing for sequence-to-sequence learning tasks. Despite their great success, they typically suffer from a shortcoming: they are prone to generate unbalanced targets with good prefixes but bad suffixes, and thus performance suffers when dealing with long sequences. We propose a simple yet effective approach to overcome this shortcoming. Our approach relies on the agreement between a pair of target-directional RNNs, which generates more balanced targets. In addition, we develop two efficient approximate search methods for agreement that are empirically shown to be almost optimal in terms of either sequence level or non-sequence level metrics. Extensive experiments were performed on three standard sequence-to-sequence transduction tasks: machine transliteration, grapheme-to-phoneme transformation and machine translation. The results show that the proposed approach achieves consistent and substantial improvements, compared to many state-of-the-art systems.

The success and popularity of deep learning is on the rise, partially due to powerful deep learning frameworks such as TensorFlow and PyTorch that make it easier to develop deep learning models. However, these libraries also come with steep learning curves, since programming in these frameworks is quite different from traditional imperative programming with explicit loops and conditionals. In this work, we present a tool called TF-Coder for programming by example in TensorFlow. TF-Coder uses a bottom-up weighted enumerative search, with value-based pruning of equivalent expressions and flexible type- and value-based filtering to ensure that expressions adhere to various requirements imposed by the TensorFlow library. We also train models that predict TensorFlow operations from features of the input and output tensors and natural language descriptions of tasks, and use the models to prioritize relevant operations during the search. TF-Coder solves 63 of 70 real-world tasks within 5 minutes, often finding solutions that are simpler than those written by TensorFlow experts.

We present a solution to real-world train scheduling problems, involving routing, scheduling, and optimization, based on Answer Set Programming (ASP). To this end, we pursue a hybrid approach that extends ASP with difference constraints to account for a fine-grained timing. More precisely, we exemplarily show how the hybrid ASP system clingo[DL] can be used to tackle demanding planning-and-scheduling problems. In particular, we investigate how to boost performance by combining distinct ASP solving techniques, such as approximations and heuristics, with preprocessing and encoding techniques for tackling large-scale, real-world train scheduling instances.

Like'digital transformation', innovation is an over-used and under-examined term. This applies within business generally, but more especially within the public sector, where there are limits to the amount of disruption and risk that it is considered acceptable to carry within the public domain. Further, a range of questions arises when government'innovates'. These include building the culture and incentives for innovation; understanding what innovation in the digital era is actually about (clue: it's not simply about having a new idea); handling the public-private sector relationship differently; scaling innovations; and handling the politics that inevitably surround changes of almost any kind to public services. The opportunity to chair the second Public Sector Innovation Conference on 25 February was a great opportunity to reflect on these, and many of the other tensions and opportunities that surround ongoing modernisation of public services, and benefit from a really high-quality speaker lineup.

What are the different types of AI enabled offers formulated by AIBridge? AIBridge possesses industry agnostic products steered for equipping organizations for Digital Transformation and Process Automation, leveraging AI and ML capabilities. We have built an artificial intelligence enabled document extraction tool named AIMunshi. By leveraging AIMunshi invoice automation tool, users will be able to seamlessly process and transfer data across file systems and documents. This could further allow the workforce to concentrate on core processes and functions, accelerate processes, and enhance customer service.