We describe several datasets and first experiments with creating conjectures by neural methods. The datasets are based on the Mizar Mathematical Library processed in several forms and the problems extracted from it by the MPTP system and proved by the E prover using the ENIGMA guidance. The conjecturing experiments use the Transformer architecture and in particular its GPT-2 implementation.

Can we infer intentions from a person's actions? As an example problem, here we consider how to decipher what a person is searching for by decoding their eye movement behavior. We conducted two psychophysics experiments where we monitored eye movements while subjects searched for a target object. We defined the fixations falling on \textit{non-target} objects as "error fixations". Using those error fixations, we developed a model (InferNet) to infer what the target was. InferNet uses a pre-trained convolutional neural network to extract features from the error fixations and computes a similarity map between the error fixations and all locations across the search image. The model consolidates the similarity maps across layers and integrates these maps across all error fixations. InferNet successfully identifies the subject's goal and outperforms competitive null models, even without any object-specific training on the inference task.

Microsoft has teamed up with a startup co-founded by Elon Musk to build one of the fastest supercomputers in the world, the company announced Tuesday during its annual Build developers conference -- held virtually this year because of the coronavirus pandemic. The startup is OpenAI, the charter of which underscores that it's working to ensure that AI which can outperform humans nevertheless benefits all of humanity. Microsoft stressed that this work represents a key milestone in a partnership announced last year to jointly create new supercomputing technologies in Azure. This is a first step, the computing giant explained, toward debuting large AI models "and the infrastructure needed to train them" as a platform that developers and other organizations can build on. "The exciting thing about these models is the breadth of things they're going to enable," said Microsoft Chief Technical Officer Kevin Scott in a company blog post about the news.

Supercomputers, like this one at Lawrence Livermore National Laboratory, are designed to tackle the world's toughest computing challenges. Microsoft has built an enormous supercomputer for artificial intelligence work, a new direction for its Azure cloud computing service. The machine has 285,000 processor cores boosted by 10,000 graphics chips for OpenAI, a company that wants to ensure AI technology helps humans. Microsoft announced the machine at its Build conference for developers on Tuesday. Supercomputers, the most powerful computing machines on the planet, are typically used for the most taxing problems. That includes jobs like simulating nuclear weapons explosions, predicting the Earth's future climate and more recently, seeking drugs to fight the coronavirus.

The proliferation of Artificial Intelligence (AI) in the Healthcare sector is one advancement that is worth a watch. Several major companies including big techs are moving forward in the same direction to revolutionize how care is being given to those in need. Recently, a collaboration between Google's DeepMind and Moorfields Eye Hospital NHS Foundation Trust has come up with a development of an AI model that has the potential to predict whether a patient will develop wet AMD within six months. In the future, this system could potentially help doctors plan studies of earlier intervention, as well as contribute more broadly to the clinical understanding of the disease and disease progression. Age-related macular degeneration (AMD) is the biggest cause of sight loss in the UK and the USA and is the third-largest cause of blindness across the globe.

Yesterday, Microsoft's Build 2020 developer conference kicked off (remotely), and we saw the first results of Microsoft's billion-dollar investment in OpenAI, a company co-founded by Elon Musk. Microsoft announced it has developed an Azure-hosted supercomputer built expressly for testing OpenAI's large-scale artificial intelligence models. While we've seen many AI implementations focused on single tasks, like recognizing specific objects in images or translating languages, a new wave of research focuses on massive models that can perform multiple tasks at once. As Microsoft notes, that can include moderating game streams or potentially generating code after exploring GitHub. Realistically, these large-scale models can actually make AI a lot more useful for consumers and developers alike.

At its Build developer conference, Microsoft today announced that it has teamed up with OpenAI, the startup trying to build a general artificial intelligence, with -- among other things -- a $1 billion investment from Microsoft, to create one of the world's fastest supercomputers on top of Azure's infrastructure. Microsoft says that the 285,000-core machine would have ranked in the top five of the TOP500 supercomputer rankings. Because Microsoft doesn't actually tell us much more than that, except for a few more specs that say it had 10,000 GPUs and 400 gigabits per second of network connectivity per server, we'll just have to take Microsoft's and OpenAI's word for this. To be in the top five of supercomputers, a machine would currently have to reach more than 23,000 teraflops per second. It's also worth noting that the No. 1 machine, the IBM Power System-based Summit, reaches over 148,000 teraflops, so there is quite a wide margin here.

Last year, Microsoft invested $1 billion in Open AI, a non-profit co-founded by Elon Musk that focuses on the development of human-friendly artificial intelligence. Microsoft announced that it has developed an Azure-hosted supercomputer built expressly for testing OpenAI's large-scale artificial intelligence models. While we've seen many AI implementations focused on single tasks, like recognizing specific objects in images or translating languages, a new wave of research is focused on massive models that can perform multiple tasks at once. As Microsoft notes, that can include moderating game streams or potentially generating code after exploring GitHub. Realistically, these large-scale models can actually make AI a lot more useful for consumers and developers alike.

In a paper published this week on the preprint server Arxiv.org, They claim that SSIs can help to solve a range of complex robotic tasks -- for example, grasping, lifting, and placing a ball into a cup -- with only raw sensor data. Training AI in the robotics domain typically requires a human expert and prior information. The AI must be tailored with adjustments depending on the overarching task at hand, which entails defining a reward that indicates success and that facilitates meaningful exploration. SSIs ostensibly provide a generic means of encouraging agents to explore their environments, as well as guidance for collecting data to solve a main task.

However, we know there's still a lot to do – this work does not yet represent a product that could be implemented in routine clinical practice. While our model can make better predictions than clinical experts, there are many other factors to consider for such systems to be impactful in a clinical setting. While the model was trained and evaluated on a population representative of the largest eye hospital in Europe, additional work would be needed to evaluate performance in the context of very different demographics. A recent study examining the use of a different AI system in a clinical setting highlighted just some of the sociotechnical issues for such systems in practice. Another difficult point to contend with is that any prediction system will have a certain rate of false positives: that is, when a patient is found to have a condition, or predicted to develop one, that they don't actually have.